Streaming media business strategies and audience-centered practices: a comparative study of Netflix and Tencent Video

Wenjia Tang; Mingou Wei

doi:10.1515/omgc-2022-0061

Article Open Access

Streaming media business strategies and audience-centered practices: a comparative study of Netflix and Tencent Video

Wenjia Tang
Wenjia Tang is a Ph.D. candidate at University of Sydney, and she also works as tutor and research assistant. She graduated from UCL, UK, with a Master’s degree in Film Studies. Her research is now on the platform industry and digital glocalization, with a particular interest in recommender system applications & regulations, pop culture in cyberspace, and global media policy.
and Mingou Wei
Mingou Wei is a Masters’s student in the School of Public Management at Sichuan University, China. Mingou’s research focuses on contingency management and social security in the 21st Century China.

Published/Copyright: March 1, 2023

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From the journal Online Media and Global Communication Volume 2 Issue 1

Abstract

Purpose

This comparative study of streaming services in different cultural and economic contexts shows how they optimize the user experience by improving recommendation algorithms, upgrading infrastructure, and developing global services, in responding to the crisis of losing subscribers.

Method

We use analysis of industry documents to demonstrate how different approaches to streaming video business value user-generated data and reconstruction practices.

Findings

There is already a similar trend among global streaming platforms to create a pan-media entertainment & cultural service by increasing revenue streams and merchandise offering categories, as well as keeping subscriptions and attracting more viewers in the long run.

Practical implications

The study displays how streaming platforms with SVOD, AVOD, and Mix-funded modes are changing their business strategies in the current hyperinflationary and increasingly competitive media market, updating how they create user-centered practices in search of ultimate commercial success.

Social implications

It also illustrates how different commercial streaming service formats end up with similar solutions to the challenges.

Originality/value

The study is a comparative analysis of the streaming phenomenon as it happens in real-time, complementing the observation and evaluation of the latest updates in the streaming industry and predicting the future trends of global brands in the digital ecosystem under different commercial and cultural logics.

Keywords: China; comparative studies; critical media industry research; Netflix; political economy; streaming media; Tencent Video; United States

1 Introduction and context

Streaming media is considered to be an updated generation of media forms (Herbert et al. 2018) and new inventions (Kaplan 2014) in the digital and internet era. By challenging the traditional distribution methods of television programs and films (Lotz 2021), streaming media uses a customized and personalized strategy (Noam 2021), applying recommendation systems to actively communicate directly with audiences and sell media viewing services and marketing brands. Currently, streaming is one of the most important forms of contemporary digital media (Lobato 2019).

The rise of streaming comes at the intersection of the digital transformation of media content production (Atkinson 2018) and the comprehensive platforming of the cultural industries (Nieborg and Poell 2018). Pan-cultural sectors, including music, film, news, radio, and other forms of communications, are being produced and stored as digital copies and distributed in national and international markets. They are strongly impacting the mass media system that has been established for decades (Murray 2016). Among the many forms, video streaming is seen as an essential example and has received a great deal of attention from academia (Dixon 2013; Flew et al. 2022; Hartley 2012).

Different business models have emerged from various video streaming media practices, including the subscriber-funded mode, also known as SVOD (subscription video-on-demand), which relies entirely on subscription revenue; the freemium/AVOD (ad-funded on-demand), which relies entirely on advertising revenue; and the Mixed-funded mode, which combines the above two features. These models highlight different levels of audience engagement, ranging from fully audience-funded to advertisers and viewers paying for their respective products to fully advertiser-funded. Thus, audience-generated data are applied for various purposes under different payment methods but are considered part of the production material in common.

This article takes a comparative approach, using the US-based streaming brand Netflix and the Chinese streaming brand Tencent Video, to emphasize the commonalities and differences between dissimilar cultural and economic circumstances of streaming video services as a common global phenomenon. The result aims to illustrate how different business practices influence and determine audience-centered financial decisions in streaming media, especially in content creation, algorithm, and infrastructure.

Netflix and Tencent Video represent a diverse range of streaming business models: one is a streaming service mainly dependent on the video production and distribution business; the other is an independent video entertainment-sharing platform owned by its parent company Tencent. Both are looking for similar growth with different subscription models. Before 2022, Netflix was a fully subscriber-funded platform, offering ad-free HD video viewing until the last quarter of 2022, when it announced a ‘Netflix Basic with ads Plan’ in selected pilot countries, providing lower price subscriptions in exchange for ad viewing hours (Peters 2022). Tencent Video, on the other hand, was first established in 2011 as an ad-funded aggregator of a wide range of original pop-culture content, offering a wide range of media and entertainment services. A year later, Tencent Video launched a new membership plan that encouraged users to pay a monthly fee in exchange for ad-free viewing experiences of premium HD video. This latest turn of Tencent Video’s business continues. And now, it allows users to choose between a free service with ads and paying service without ads.

Netflix and Tencent Video are the top streaming service providers in terms of subscriptions in the US and China, respectively. Netflix has over 230 million subscribers worldwide (Statista 2023) and ranks No. 1 in the world in terms of subscriptions; meanwhile, Tencent Video obtains over 161 million subscribers worldwide (Tencent 2022c) and ranks No. 4. Defining themselves as transnational companies, Netflix and Tencent Video have launched their international-level business for the global market, partnering with overseas media production companies, creative industry practitioners, and local TV channels to commission and invest in the production of original titles and then distribute these TV shows and films in multiple countries with both local cultural styles and global-level production (Keane et al. 2022; Lotz 2022). Both Netflix and Tencent Video have developed distinctive streaming formats in doing business with their respective large numbers of subscribers. They value and actively exploit the impact of user interaction data on digital content production and distribution, algorithm adaptation, and industrial organization. Therefore, the practices of the two brands can serve as a reference for the diversification of streaming platforms in the international digital market.

As a new technological tool (Kaplan 2014) and business model (Srnicek 2016), video streaming challenges traditional media operations by re-examining the relationship between the platform itself, the audience, big data, digital labor, and commodities. From a political economy perspective, these fluid shifts in power give rise to a real-time reflection on streaming as a phenomenon of commercial and cultural application, with the fundamental questions being asked: Firstly, how to define the audience in the context of digital participation. Secondly, what is the nature of the audience’s (cultural) work, and what do they get in return for their interaction? Third, from a commercial perspective, how can streaming platforms balance audience interest and succeed in a dynamic relationship that lasts over time? The article will analyze how streaming brands in different business modes reposition themselves concerning their audiences in the production and distribution of digital content. The key argument is that streaming services should recognize audiences’ value in data generation, content creation, and distribution, build user-centric recommendation systems, and train algorithms to provide effective analytics results to help streaming brands keep subscribers and attract more. It is also the key for streamers to gain an edge in the global marketplace in the future.

2 Theoretical framework and methodology

Viewing the internet as a democratic space in which all people can participate, Keltie (2017) illustrates how the twin conditions of digital technology and media convergence have brought about changes in the foundation of media production and consumption: audiences (whether conscious or unconscious) are involved in contributing to digital content and transforming parts of their behavior into labor values that are integrated as part of the commodity. Based on these characteristics, consumers in the platform era are described as interactive audiences (Jenkins 2006), empowered audiences (Hesmondhalgh 2010), and democratic audiences (de Beus 2011). These definitions demonstrate the importance of audiences regarding digital goods in Internet commerce and highlight research on the importance of audiences as a digital workforce.

Audience research (Turow 2013; Wayne 2021) and unpaid digital labor (Fuchs 2014) were essential issues of early-stage platform studies and provided a different perspective on how to view the relationship between users/consumers and service providers/markets. However, a distinction should be made between on-demand media and consumer engagement, as opposed to other types of media with UGC (user-generated content) contributions, where the audience does not directly participate in creating and sharing content. Therefore, when discussing the evolution of power between streaming platforms and audiences, it is crucial to figure out which categories of audiences are the subject. Such as Netflix, where content is created and circulated entirely by professional producers and teams, the audiences’ role is not the same as that of Youtube and TikTok, which rely heavily on user-uploaded content and amateur creations. Even though all platforms connect with digital audiences’ participation, whether consumers are directly involved in content production will define what kind of business they are in (Cunningham and Craig 2016).

Referring to the classic discussion of the cultural industries, Adorno and Horkheimer (1993) give a more homogeneous assessment of the audience: they see the audience as passive consumers of products and texts. This historical perspective is based on the recognition of the identity and creative contribution of the producer, and more credit goes to the production team, the platform, and the investor. However, as the platform economy and business of attraction (López García et al. 2019) increasingly rely on big data and the recommender system (Marciano et al. 2020), subscribers to streaming video services should not simply be defined as recipients of culture and objects of exchange of goods. Because audiences participate in the exchange of products and services, they are one generator of big data, and thus become part of the production.

This procedure of audience interaction in the production of data can be discussed in two parts: active data production, i.e., data that are added and recorded through the spontaneous actions of users such as searching, subscribing, commenting, etc.; and passive data production, i.e., data that viewers produce through feedback with the system, including pausing viewing, leaving the page, accepting recommendations, etc. Both active and passive data production is relevant to the commercial success of streaming media (Marr 2015). Platforms want to make the most efficient use of data and keep subscribers, while subscribers expect a better media experience and initiative in data production and content production in return (Plothe and Buck 2019).

Considering the technological changes in the field of media communication and the renewal of industrial structures, debates on whether audiences should be seen as commodities (Wasko 2018) or currencies (Moro Visconti et al. 2017) are happening. Using real-time streaming practices as research subjects, political economists are concerned with ownership and control of the industry (Curran and Hesmondhalgh 2019) and are actively engaged in discussions about who should lead business decisions and branding strategies in digital, particularly streaming media. The overlapping view comes from critical media industry researchers (Havens and Lotz 2012). However, they pay more attention to the intrinsic relationship between commodities and industry structures and attempt to clarify the links between people (including producers, audiences, advertisers, etc.), content/products, and capital in the industry from a micro-level. Both perspectives bring the audience – the key source of big data and the object of media consumption – into the frame of discussion, considering how to define the value of the audience for streaming brands in a specific historical period and geographical space. At the same time, the interrelationship between different factors of production is respected, and the commercial initiatives of streaming providers to increase audiences are included in evaluating their industry strategies.

Based on these theories, this article examines the impact of audience activity and the data they actively/passively generate on the content production and distribution strategies of on-demand media and subsequently predicts the possible involvement of audiences in the future by combining the real-time practices of Netflix and Tencent Video, brands from the Global North and South. This comparative study collects and analyses data from varied business models and cultural markets. The result is a complement to Western-centric perspectives on the business practices of digital media. It enriches the application of political economy and media industry studies to critique the business models of streaming media, enhancing the understanding of audience-generated data as an important contributor to digital content production.

Using the document analysis method, this article provides insight into the published industrial reports and financial data of Netflix and Tencent Video. The information offers solid evidence for understanding how streaming media develop effective business strategies based on the generation and use of data during viewer engagement, while industry files also re-examine the real-time decisions made by streaming brands concerning updated user relationships. Combined with the documentation of market feedback, such as user reports, these data help to understand how platforms balance audiences’ interests and their commercial success.

The dominant approach of industry analysis focuses on combining industry-specific developments with the expectations of the target market (Baker 1973), discussing existing and potential future business decisions and how regulations may help companies achieve desired outcomes. This articleused a case study approach to understand how Netflix and Tencent Video, in the current global market of streaming media, take advantage of platformisation and users’ engagement as resources for basic algorithms and fuel for recommendation systems (Schintler and McNeely 2020). This closed-loop system facilitates the flow of further quality services and satisfaction to the media audience (Kim et al. 2021). It subsequently constitutes the key to commercial success in their future target markets.

We propose a critical media-industry analysis structure to illustrate that the use of big data and the reconceptualization of the power relationship with the audience are rooted similarly across the different business models of streaming media. This method is highly practical for tracking and updating market data and audience feedback in real-time. Additionally, the analytical structure is a cross-comparative model that can help assess the sources of difference in business strategies between the two brands and provide text on the conditions of implementation, effectiveness, and prospects for possible future changes. The comparative study findings will be an important tool in explaining streaming media business strategies.

This article uses public filings from 2013 to 2022, including quarterly and annual reports of Netflix and Tencent Video, subscriber agreements, research articles published by Netflix Research, user experience reports, trade press with relevant industry information, public statements by spokespeople, and secondary information such as news reports. The study focuses on current industry dynamics and changes over the last three years (2020–2022) and therefore emphasizes recent public material. The analytical approach reflects a continuous focus from macro to micro, not only comparing the general practices of streaming platforms with different business models in the background of a similar global digital platform industry, but also focusing on the distinctions between several cultural and economic markets. Therefore, the qualitative approach to comparative research is a reasonable choice.

3 Different business strategies and comparative study

The digital transition of the media industry and the rising popularity of online video viewing have witnessed a change in the relationship between content providers and content consumers. Streaming media has long been seen as a competitor and successor to cable TV (Shattuc 2020). Still, the only thing it inherits from TV is the form of content, i.e., TV shows, reality shows, and documentaries. The merchandise streaming peddles is distinctly different from traditional television or cinema: Television sells content, such as an episode of a TV series; however, streaming sells subscription service (Lotz 2022). Streaming as a platform no longer worries about the success of a single show, but about retaining the maximum number of subscribers and attracting new ones.

Streaming media under different fee models are responsible for various parties. SVOD ones only respond to their subscribers, aiming to produce content that spans a long time and can match subscribers with the best of their interests in real-time (Weidhaas et al. 2021). Therefore, subscription consumption constitutes (almost) the entire revenue. For this type of streaming, it is crucial for platforms to produce content that reaches the audience and meets their aesthetic requirements. Relatively accurate recommendations are essential. Brands in the AVOD mode are responsible for advertisers, as advertising revenue determines what content can be produced and which group of audience it is distributed to. In that case, the viewer’s attention (Sherman 2020) and the duration of that attention are sold as a commodity. Advertisers decide whether and how to invest on the basis of the audience data they receive. Besides, in the Mixed-funded mode, however, satisfying the user’s tastes and maintaining the user’s attention are both important to the commercial success. As a result, according to critical media industry research (Havens et al. 2009), the source and composition of funding, i.e., the proportion of subscriber revenue and advertising revenue, needs to be clarified to evaluate a mixed-funded streaming video service, as this will determine the main products traded by these streaming platforms and the nature of their business. See [Table 1].

Table 1:

Streaming media services under different business models.

Streaming service mode	Source of income	Commodities	Key to success
SVOD	Audiences’ subscription fee	Streaming services in general; the library collection of digital content, etc.	Satisfactory recommendation results; high-quality shows, etc.
AVOD	Advertisers’ investment	Audiences’ attentions; possible subsequent purchase behaviors, etc.	Large subscription number; visibility; loyal audiences; reputation, etc.
Mixed-funded	Both subscription and advertising	Streaming services; content; audiences’ attentions; click and browse; possible subsequent purchase, etc.	All mentioned above

As one example of the platform economy, streaming aggregates content producers, advertisers (if they exist), and consumers together. However, in different models, these three objects are assigned variable identities. For example, in the SVOD mode, data is an essential means of production, and consumers are seen as part of the production resource and make key contributions to data collection and presentation. Therefore, the audience is given the role of a producer and a consumer. In the AVOD mode, on the other hand, data are advertisers’ primary object of trust and consumption (Barreto and Murdock 2021). Therefore, as the data source, the audience is passively part of the commodity. In this case, the audience is both a consumer and a product. The role of the audiences is fluid, changing, and multiple across the different service models and stages of service delivery in streaming media.

The users’ engagement with any content on the screen is accumulated and stored as raw data in a personal digital profile, from active data generated by the user’s input of content and resulting behavior to passive data generated by computer software that mines the content and asks for a relative response from the user. All user data are for generating detailed profiles of their likes and preferences and for advertisers looking to promote their brands more accurately and effectively. This article will illustrate how two iconic brands, Netflix and Tencent Video, have recognized the value of audience-generated data for business strategies and then built user-centric recommendation algorithms to help achieve commercial triumph.

3.1 Netflix

Since founded in 1997 in Scotts Valley, California, beginning as a DVD-mailing rental service. Until launched a new streaming service in the U.S. in 2007, Netflix initially defined itself as a subscriber-funded video business. The business model is characterized by Netflix providing a high-quality service completely free of advertising and opening up the entire (regionally) available database, or library of title collection, to viewers, who pay a fixed monthly fee to support the service. As a result, subscription fees from viewers represent the majority of SVOD revenue (Netflix Inc. 2021). According to Netflix’s (2022) Q4 Financial Statements, interest & other income accounted for only 2.2% of total revenue, while the rest was subscription revenue. Thus, Netflix’s dependence on subscribers in this model dictates that the ultimate goal of its business is to maximize the number of existing subscribers by encouraging long-term subscriptions and attracting potential new audiences.

In the early stages of development, Netflix executives believed that the key to maintaining subscribers’ continued interest in the service was the satisfaction of their entertainment needs (Hallinan and Striphas 2016). The principal element of this was the ability of the recommendation algorithm to deliver precisely the results viewers wanted. With this logic in mind, Netflix first encouraged computer engineers, mathematicians, and algorithmic scientists to participate in the first programming competition, called the Netflix Prize, to reprogram its recommendation system to ensure that content is better matched to viewers’ interests. The Netflix Prize was a gimmick with a one-million-dollar prize. The competition did help Netflix initially build an industry-leading video recommendation system, which has been refined over the years. In post-contest surveys, Netflix received the highest score and was ranked No. 1 among the online satisfaction list reported by United Press International (Schuman 2007). In addition, Netflix’s subscription revenue exceeded $1.2 billion in 2007, compared to $682 million in 2005 (Ruby 2022). Even considering Netflix’s transition from a mailing physical copy service to an online streaming service in the year following the competition, the growth in revenue during this period was enormous. Optimizing the recommendation algorithm and subsequent positive feedback gave Netflix the confidence to complete the commercial transition and helped it firmly establish its user data-centric strategy (Burroughs 2019).

Building on the raw data it had accumulated from its DVD-by-mail service, Netflix launched a streaming experience that quickly became a success, beating out its old rival Blockbuster in short order (Marketline 2011). Sensing the superiority of video streaming services, in 2011, Netflix completed the whole ‘going online’ path, abandoning its offline business and embarking on a new strategy: producing original content. In 2012, Netflix released its first original series, Lilyhammer, and released all episodes at once. This was an innovative move that has since been replicated by other streaming platforms. The following year, Netflix published the original series that made it more famous, House of Cards.

Optimizing its recommendation algorithms and business strategy brought Netflix more subscribers, which generated more interactive data that it valued and used in its subsequent content creation and production. In 2013, Netflix collected and calculated data from 30 million viewers and 4 million reviews (Carr 2013), as well as judging the performance of previous films, and decided to combine director David Fincher, with actor Kevin Spacey, in a remake of the political drama House of Cards. The series received an overwhelming global hit and an increase of 3 million subscribers (Petraetis 2017). This was a direct example of translating audience demand into content production. It has since opened up the industry and academic attention to Netflix’s success in using audience behavior data to predict interests and put them into production in the cultural industry. Since then, Netflix’s name has been successfully associated with big data; the audience behind the data, as a source of the contribution, has gained a more critical role in media production.

Netflix has replicated the success of House of Cards several times in the years since, using similar data analysis to invest in popular titles. After collecting, calculating, and applying viewer behavioral data and gaining positive economic results, Netflix has become more focused on the importance of large amounts of user data to its business success and building a user-centric experience on all fronts. This included upgrading its infrastructure. In the pre-streaming era, Netflix stored its initial data on several large Oracle servers with Java front ends in California. However, in 2008, Netflix experienced significant database damage, leading to a three-day suspension of operations. This loss made Netflix executives realize that a reliable, horizontally scalable system was required to protect the long-term storage and efficient use of data (Farrow 2011). Accordingly, Netflix chose AWS, a cloud storage service owned by its competitor Amazon, as its new data center. This data migration has allowed Netflix to retain existing subscribers and has led to an exponential increase in good viewing interactions for users, thus meeting the need for more data production and storage (Amazon Web Services 2016).

The use of AWS provides the technological foundation for Netflix to scale rapidly, operate securely, and fulfill the capacity requirement anywhere in the world. Additionally, AWS allows Netflix to communicate with artists and viewers worldwide without the limitations of technology or geographic barriers. As of 2022, Netflix is already present in over 190 countries and territories, and AWS’s global distribution helps it address the need for computing power in different markets. The AWS services are complemented by a Netflix-owned content delivery network (CDN) named Netflix Open Connect, which provides a better and smoother video viewing experience for subscribers globally (Amazon Web Services 2023).

By partnering with Amazon’s cloud storage service and transferring user data, Netflix chose to secure the data and maintain efficient data analysis. With the data transfer, Netflix realized that the security and efficiency of storing and processing user data are a prerequisite and the heart of all their business activities. Netflix’s commitment to user data and the development of a user-centric infrastructure and distribution network is also evident in the vast annual expenditure of over $1 billion to AWS.

However, hardware and software updates will not help Netflix stay competitive in the global streaming market forever. As more similar businesses enter the market, such as Amazon Prime Video and Disney plus, brands that also use big data to create user-centric streaming services have impacted Netflix’s novelty and diverted some of its subscriber base (Lobato and Lotz 2021). Moreover, with the end of the COVID-19 pandemic lockdown and market inflation, Netflix saw a loss of subscribers and a decline in willingness to subscribe (Flew and Park 2022). Algorithm optimization and infrastructure alone are no longer enough to help Netflix regain its dominance in the market.

Netflix reported a significant drop in subscriptions in the first quarter of 2022, the first decline in a decade (CNN Business 2022). As more brands, such as HBO Max and Hulu, allowed subscribers to watch ads in exchange for the video viewing experience at a lower price, Netflix also began to wonder if it needed to add another tier of service in an attempt to mitigate the loss of subscribers. The debate continued for several years. That is until Reed Hastings said in a public speech in April 2022:

Those who have followed Netflix know that I have been against the complexity of advertising and am a big fan of the simplicity of subscription. But as much as I’m a fan of that, I’m more of a fan of consumer choice. And allowing users who would like a lower price and tolerate advertising to get what they want makes much sense (TechCrunch 2022).

This statement expresses Netflix’s reluctance to switch its business format, but respecting the needs of its subscribers, a plan to exchange ad viewing for lower subscription prices is an imminent action. Therefore, in November 2022, Netflix began offering a new Ads Tier service, allowing subscribers to pay a lower monthly fee in exchange for watching 4 min ads per hour (Netflix 2022). Netflix named this new plan “Netflix Basic with ads Plan” in its 12 major markets (including the US, UK, Canada, Mexico, and Brazil) as pilots and designed it to evolve the plan and launch it in more markets. In its home market the United States, a subscription to the ad-based Netflix service can save $3 per month on the base subscription fee. The plan differs from converting Netflix into a simple AVOD business but introduces multiple planners, allowing subscribers to choose their type of service. Therefore, Netflix’s new initiative can be seen as a different kind of Mix-funded business.

Furthermore, Netflix has other plans to increase potential subscribers, including the newest-announced cancellation of password sharing, which used to be one of the main attractions (The Wall Street Journal 2022). Netflix requires non-family members to pay for additional logins. The backdrop for the plan is also Netflix’s concern that multiple account sharing will negatively impact the subscriber loss they experienced in 2022. The stop password sharing act has not yet been made public regarding how and where it will be implemented. However, some media outlets have made predictions based on published information (CNET 2023). The news gained much attention on social media and quickly became a trending topic, with many users reporting negatively on the news (Stuff 2023). Since Netflix has always positioned itself as the leading brand for home entertainment (Mulla 2022), this initiative was attacked for going against the brand’s positioning. While Netflix has introduced several features that encourage shared entertainment experiences, such as 2020 renamed expanded service Teleparty (formerly known as Netflix Party), a free chrome extension that allows friends in different locations to synchronize their video viewing and can be used for content libraries from multiple streaming brands; this feature has not really been in the audiences’ favor, especially since Netflix decided to scrap its password sharing service. This is because off-site users need an extra active Netflix account to log in. Consequently, this function aims to make Netflix a popular social media and a necessary means of maintaining relationships and connections. People not part of this social circle feel forgotten or excluded (Jenner 2018) and are forced to subscribe to Netflix to have everyday conversations.

Netflix has not yet responded to these criticisms, but it is clear from these two important changes for Netflix that the platform is most anxious to maintain its subscriber base, even if some of its strategies are not directly aligned with viewers’ demands in their current form. The subsequent impact of these two changes will not be observed until they are formally and widely implemented. It will be necessary for academia to keep an eye on whether Netflix modifies and responds to these strategies in the next year or two.

3.2 Tencent Video

De Kloet et al. (2019) comment that the current generation is witnessing the rapid progress of China’s social platform. Similarly, Curran and Hesmondhalgh (2019) highlight that China is now becoming a key player in the global media and technology scene. Supported by the government’s ‘Internet+’ policy (Keane 2016), China now has a fast-growing and ubiquitous array of platforms: Payment, transport, housing, culture and entertainment, and other items essential to everyday life are available in the form of platforms (e.g., portals or smartphone apps). The boundaries between platforms and public infrastructure are becoming increasingly blurred, and platforms are now integral to everyday life. These platform practices and technology companies from China provide the Chinese logic for developing the Internet-sharing economy. They are different from the Western method represented by the United States, which adds a diverse sample of global practice. In 2016, the General Office of the State Council of PRC announced four phases of a national platform strategy, including trade connectivity, payment and e-currencies, social credit systems, and smart city infrastructure, to begin, highlighting the trend of shifting control of trade in goods and services from the state to digital platforms.

Against the background of international and domestic platform-based strategic developments, Tencent Video, the cultural and entertainment services industry owned by Chinese internet technology giant Tencent, has taken the lead in becoming China’s pioneer video service, providing users with an online HD video viewing experience, thanks to a fully modernized infrastructure and advances in mobile communication technology (de Kloet et al. 2019).

Tencent Video first launched its business in China in 2011. At its inception, Tencent Video was a completely free service, offering a selection of TV series, films, and news clips. Audiences did not need to sign up for an account or purchase a subscription to access the entire collection. In addition, the Tencent Video platform allowed individual users to upload their content, including video clips and some independently filmed tapes. Thirty-second to 1 min ads automatically accompanies all content uploaded to Tencent. Advertising revenue was the main source of income for Tencent Video in the early years. In 2011, Tencent reported advertising revenue of approximately 2 billion RMB (equivalent to 293 million USD), representing 19.6% of total revenue for the year (Tencent 2015). As Tencent does not report on its separate video business, we are unable to obtain accurate information on Tencent Video’s advertising revenue in its first year. However, the annual report shows Tencent’s confidence in this fledgling video entertainment business.

In 2012, Tencent Video launched a value-added service called “Hollywood Membership,” which allows subscribers to access selected movie content and enjoy ad-free experiences. Moreover, subscribers can personalize their Tencent Video account. This “Hollywood membership” was the predecessor of Tencent Video VIP. After a strategic change, Tencent Video eventually defined itself as a streaming service aggregator operating a full range of video content, offering not only TV shows and films but also anime, music concerts, live sports, online classes, live games, and other diversified products. After canceling the ‘Hollywood membership’; Tencent Video launched a subscription service called Tencent Video VIP, which charges 30 RMB (about 4.4 USD) per month and offers a full range of video content on its platform.

It was in the mid-to-late 2010s that several Chinese streaming platforms, including Tencent Video, finished restructuring their businesses and started commercial activities in an orderly manner. As a result, Chinese audiences are not familiar with the model of paying for digital video content (Liu et al. 2015). Additionally, unlike the United States audiences, who have a long history of paying exorbitant fees for cable TV, the Chinese spend very little money each year to watch television programs (Oba and Chan-Olmsted 2005). Consequently, many Chinese audiences are uncomfortable with the idea of paying extra for digital content as the trend of video products going online. In this case, Tencent Video has allowed some of its content to be made available for free to unsubscribed viewers (also referred to as ‘Visitors’). Currently, Tencent Video only offers a small percentage of its entire library of free content, including some TV series, films, and children’s content. Visitors are required to watch between 45 and 90 s of ads per episode in exchange for access.

Tencent Video’s complex subscription rules make it difficult to categorize it as a standalone SVOD or AVOD service. Due to its diverse revenue streams, Tencent Video is seen as a Mix-funded on-demand platform. Although Tencent Group rarely directly discloses commercial data related to Tencent Video due to the involvement of advertisers, some third-party documents are publicly available to help understand the composition of Tencent Video’s revenue. In 2017, Tencent Video reported 43 million subscribers. It also claimed that the paid subscription revenue only accounted for 30% of its overall income (South China Morning Post 2017). Therefore, in the late 2010s, Tencent Video’s primary source of revenue was advertising, although they also received some direct money from audiences.

As Tencent Video increases its investment in original content and enriches its library (Lin 2022), it has seen a significant increase in subscribers. By 2021, Tencent reported 124 million subscribers, almost three times the number in 2017. According to the Tencent 2021 annual report, Tencent Video has consolidated its number one position in China with its diverse content in anime, drama series, and sports games. Meanwhile, Tencent is implementing cost-optimization processes, along with expanded sales channels and high-quality content and services, to maintain its leadership while reducing financial losses. However, while subscriptions are increasing, Tencent is facing a crisis with overall media advertising revenues continuing to decline, falling by 25% to 3.2 billion RMB (approximately 469 million USD) in 2021. By the third quarter of 2022, advertising revenue had dropped again to 2.6 billion RMB (about 381 million USD). According to a report published by App Growing (2021), the proportion of advertising revenue generated by Tencent’s cultural and entertainment services (excluding games) to total revenue in 2021 is only 11.1%.

To recover from this significant drop in advertising revenue, Tencent Video has emphasized the quality and growth of its membership subscriptions in recent years (Tencent 2022a, 2022b) and has embarked on a series of user-focused reforms. These include using big data and user behavior records, reviews, social media word-of-mouth, and other information to customize its TV and film content better to match the tastes of the Internet-age audience. A trend already in place is that Tencent Video will adjust its production strategy for the following year based on the hot titles from previous years’ episodes to bring it as close to viewers’ preferences as possible. Tencent Video publishes the genres, categories, and sometimes IPs that it expects to produce in the coming year at its annual launch event in the middle to end of the year, as well as a list of alternative casts, and makes them discussed online. Meanwhile, Tencent Video also collects real-time feedback on the projects from social media groups, pro and con comments, and reposts. The data are combined with previous behavioral records, such as the number of clicks, plays, pauses, and likes of similar content on Tencent Video, to inform the emergence and modification of new proposals. For example, in 2019, the costume martial arts drama Qing Yu Nian (庆余年, 2019), majorly invested by Tencent Pictures, received much attention in Chinese social media. The series was adapted from online literature of the same name published on Qidian.com, an online reading platform owned by Tencent group, and premiered on Tencent Video. Due to the success of Qing Yu Nian, Tencent Video replicated this template and, in 2021, produced another costume action-drama series based on another online martial arts novel, Sword Snow Stride (雪中悍刀行, Xuezhong Handaoxing, 2021), casting with the same lead actor from Qing Yu Nian. The series was co-produced by China Central Television and Tencent Penguin Pictures, a subsidiary of Tencent group, and broadcast on Tencent Video as the exclusive online distribution platform. As a result, audience feedback and user data influence Tencent Video’s choice of content production, and it has a degree of say in allocating creative resources. This procedure is guided by audience interaction: the platform expects audiences to provide more timely opinions and reflect their interests and tastes, and the platform offer products that meet the aesthetic expectations of viewers and promptly satisfy their preferences and demands for a particular type of cultural commodities.

Additionally, unlike Netflix, the viewership and click-through rate of Tencent Video’s shows and movies are publicly available. Advertisers can quickly obtain reliable data from the front database and survey companies. For advertisers working with Tencent Video, three main online advertising billing methods can be used to calculate exposure and advertising costs: 1) CPM (Cost Per thousand impressions), 2) CPC (Cost Per thousand Click-thousand), and 3) CPA (Cost Per Action). These three models are based on the number of views, clicks to the ad page, and direct spending from the ad page, respectively. Obviously, the higher the number of clicks and watch, the more excellent the opportunity for advertising to do their work. This not only protects the interests of Tencent Video as an intermediary platform but also allows the platform indirectly to benefit from the viewers’ behavior of watching ads, clicking on ads, and leading consumption of ads. Therefore, the level of advertising revenue is closely related to user behavior and the number of subscribers.

Apart from content updates, Tencent Video also recognizes the importance of infrastructure establishment and updated internet technology to cater to the quality viewing experience of streaming media on mobile devices. As a result, more than a million cloud servers have been set up in Tencent’s key markets, such as several major Asian cities – Beijing, Shanghai, Chengdu, Tokyo, and Bangkok – which are responsible for Tencent Video’s data storage and operational computing. The usefulness of setting up base stations in major cities is apparent: Tencent takes advantage of a wide range of acceleration nodes to deliver the content it needs steadily, without the resulting delays caused by network congestion, operator differences, cross-region, cross-time zones, etc. This means that viewers can confidently use the Tencent Video app on their mobile devices and have a high-quality and smooth video viewing and downloading experience (Tencent Video 2021). The construction and layout of the servers reflect Tencent’s mapping of the geography of its users and show an understanding of the composition of its user base. This information may be fed by big data, but it influences the Tencent Group’s infrastructure blueprint in both directions. The Tencent Video User Agreement 7.2.1 states that: “You (the audience) understand and agree that to provide you with an effective service, the software will utilize the processor and bandwidth resources of your computer equipment (2021)”. The audiences will also be required to pay the cost of their privacy in exchange for a good service. While China and the United States have achieved mainly freedom in hardware production, streaming video platforms are added to the system as part of the software, along with cables, base stations, satellites, and other communications equipment to form a larger interconnected world media picture.

In 2019, after accumulating a large amount of excellent original content, Tencent Video launched its first overseas version of WeTV, which provides service in 11 languages and is currently running in most Asia-Pacific countries. WeTV has partnered with many cultural markets in Southeast Asian countries, such as CH3 in Thailand and Media Prima Group in Malaysia, to produce local TV series and film titles. WeTV has already garnered over 25 million daily views in Southeast Asia (Lifestyle Asia 2021). WeTV’s move to seek out overseas viewers is also a symbol of Tencent’s judgment that the domestic market is becoming saturated and businesses need to grow externally to attract more viewers. Retaining and growing subscribers is critical to both direct viewership revenue and indirect advertising revenue. Tencent also recognizes that the number of subscribers will be a key point of competition in the future. Although as a late starter in Chinese streaming, Tencent Video’s strategy of ‘going abroad’ will not be as competitive as Netflix and other Western brands in the international market, it does make a change. Tencent Video not only focuses on its domestic audiences of over 120 million but also tries to attract more potential international viewers, and generates and collects data on users in a broader context. This challenge is interesting enough.

4 Conclusion

The cases of Netflix and Tencent Video illustrate how streaming services with different business models value the importance of user-generated behavioral data, both active and passive, for the production and distribution of digital content. By improving their recommendation algorithms, updating infrastructure, and developing global services, streaming platforms are optimizing the user experience, driving more audiences to their subscriptions for longer terms, as well as attracting potential new viewers to pay for the service. This is critical for streaming brands to compete in the marketplace in the future.

The trend is that the leading streaming brands are already facing the threat of market saturation due to inflation and the entry of more competitors. They need to transform themselves quickly to avoid losing more subscribers. In addition to continuing to operate in a global market, Netflix’s current strategy for dealing with this is to add cheap plans with ads, stop password sharing, and introduce new functions (such as social and Netflix Games). Netflix is playing a game between trying to satisfy its audience and satisfying advertisers. These measures are still in their early stages of implementation; therefore, their effectiveness should be judged in the upcoming future. Tencent Video, as a genre whose primary income is advertising, has adopted a similar but different strategy to Netflix in the face of the crisis of a saturated domestic market and declining advertising revenues. The similar method is to launch services for vast overseas markets, producing content in collaboration with local TV stations and creators to enrich the library’s collection further. The different strategy is to focus more on revenue from subscribers, meeting their needs as much as possible to increase audience revenue. See [Figure 1].

Figure 1:

Streaming services changed business strategies.

While Netflix and Tencent Video had disparate beginnings, they are currently moving toward each other’s status. They begin to adopt a changing but not entirely new model of practice in the streaming market for the foreseeable future. This hybrid business model will not happen with streaming start-ups for a while, as their primary goal is to offer a diverse range of services to complement giants rather than competitors. However, this trend has become inevitable in the digital media industry, especially when brands reach an advanced stage of development. They are urgent to go beyond a single business model and a single market to make a breakthrough.

These two streaming platforms from varied cultural and economic needs are already showing similar trends and will continue to validate the possibilities of a multi-revenue, multi-commodity, pan-media entertainment, and cultural business. Both are currently struggling to balance the interests of their subscribers with the potential for more value-added services and commercial revenue, and there are no immediate results in the short term for content. However, both Netflix and Tencent Video demonstrate that the relationship between digital media platforms, capital, and audiences needs to be redefined in the course of the commercial activities of streaming services and that the changing power relations between them need to be placed in a specific setting (business model, time, specific cultural market, etc.). This article hopes this commonality will be guided by similar economic rationale and aims to provide comparative case studies for subsequent streaming research.

Corresponding author: Wenjia Tang, Department of Media & Communications, FASS, University of Sydney, Sydney, Australia, E-mail: wtan8518@uni.sydney.edu.au

About the authors

Wenjia Tang

Wenjia Tang is a Ph.D. candidate at University of Sydney, and she also works as tutor and research assistant. She graduated from UCL, UK, with a Master’s degree in Film Studies. Her research is now on the platform industry and digital glocalization, with a particular interest in recommender system applications & regulations, pop culture in cyberspace, and global media policy.

Mingou Wei

Mingou Wei is a Masters’s student in the School of Public Management at Sichuan University, China. Mingou’s research focuses on contingency management and social security in the 21st Century China.

References

Adorno, Theodor & Max Horkheimer. 1993. Dialectic of enlightenment. http://www9.georgetown.edu/faculty/irvinem/theory/Adorno-Horkheimer-Culture-Industry.pdf.Search in Google Scholar

Amazon Web Services. 2016. Netflix case study – AWS. Amazon Web Services, Inc. Available at: https://aws.amazon.com/solutions/case-studies/netflix/.Search in Google Scholar

Amazon Web Services. 2023. Data lakes and analytics | AWS. Amazon Web Services, Inc. Available at: https://aws.amazon.com/big-data/datalakes-and-analytics/.Search in Google Scholar

App Growing. 2021. Tencent’s advertising industry share. Available at: https://appgrowing.cn/.Search in Google Scholar

Atkinson, Sarah. 2018. From film practice to data process. Edinburgh: Edinburgh U.P.10.3366/edinburgh/9780748693580.001.0001Search in Google Scholar

Baker, Samuel. 1973. Risk, leverage and profitability: An industry analysis. The Review of Economics and Statistics 55(4). 503–507. https://doi.org/10.2307/1925675.Search in Google Scholar

Barreto, Helena & Murdock Graham. 2021. The critical political economy of communications in critical times interview with Graham Murdock. International Electronic Journal of Political Economy of Information, Communication and Culture 23. 83–97.Search in Google Scholar

Burroughs, Benjamin. 2019. House of Netflix: Streaming media and digital lore. Popular Communication 17(1). 1–17. https://doi.org/10.1080/15405702.2017.1343948.Search in Google Scholar

Carr, David. 2013. Giving viewers what they want. The New York Times. Available at: https://www.nytimes.com/2013/02/25/business/media/for-house-of-cards-using-big-data-to-guarantee-its-popularity.html.Search in Google Scholar

CNET. 2023. Netflix’s password-sharing crackdown has come: Everything to know. CNET. Available at: https://www.cnet.com/culture/entertainment/netflixs-password-sharing-crackdown-has-come-everything-to-know/.Search in Google Scholar

CNN Business. 2022. Netflix is back to growing after a nightmare year of losing subscribers. Available at: https://edition.cnn.com/2022/10/18/media/netflix-earnings/index.html#:∼:text=Netflix%20reported%20in%20April%20a.Search in Google Scholar

Cunningham, Stuart & David Craig. 2016. Online entertainment: A new wave of media globalization?—Introduction. International Journal of Communication 10. 5409–5425.Search in Google Scholar

Curran, James & David Hesmondhalgh. 2019. Media and society, 6th edn. London: Bloomsbury Academic.10.5040/9781501340765Search in Google Scholar

de Beus, Jos. 2011. Audience democracy: An emerging pattern in postmodern political communication. In Kees Brants & Katrin Voltmer (eds.), Political communication in postmodern democracy, 19–38. London: Palgrave Macmillan.10.1057/9780230294783_2Search in Google Scholar

de Kloet, Jeroen, Poell Thomas, Zhe Guohua & YiuFai Chow. 2019. The platformization of Chinese Society: Infrastructure, governance, and practice. Chinese Journal of Communication 12(3). 249–256. https://doi.org/10.1080/17544750.2019.1644008.Search in Google Scholar

Dixon, Wheeler. 2013. Streaming: Movies, media, and instant access. Lexington: The University Press of Kentucky.Search in Google Scholar

Farrow, Rik. 2011. Netflix heads into the clouds interview with Adrian Cockcroft. Login 37(1). 1–3.Search in Google Scholar

Flew, Terry & Sora Park. 2022. Willingness to pay: The future of screen entertainment. Content Futures 50(1). 26–29.Search in Google Scholar

Flew, Terry, Holt Jennifer & Thomas Julian. 2022. The SAGE handbook of the digital media economy. New York: SAGE Publications, Limited.10.4135/9781529757170Search in Google Scholar

Fuchs, Christian. 2014. Digital labour and Karl Marx. New York: Routledge.10.4324/9781315880075Search in Google Scholar

Hallinan, Blake & Ted Striphas. 2016. Recommended for you: The Netflix Prize and the production of algorithmic culture. New Media & Society 18(1). 117–137. https://doi.org/10.1177/1461444814538646.Search in Google Scholar

Hartley, John. 2012. Digital futures for cultural and media studies. Chichester: Wiley-Blackwell.10.1002/9781118106723Search in Google Scholar

Havens, Timothy & Amanda Lotz. 2012. Understanding media industries. Oxford, UK: Oxford University Press.Search in Google Scholar

Havens, Timothy, Amanda Lotz & Serra Tinic. 2009. Critical media industry studies: A research approach. Communication, Culture & Critique 2(2). 234–253. https://doi.org/10.1111/j.1753-9137.2009.01037.x.Search in Google Scholar

Herbert, Daniel, Lotz Amanda & Marshall Lee. 2018. Approaching media industries comparatively: A case study of streaming. International Journal of Cultural Studies 22(3). 349–366.10.1177/1367877918813245Search in Google Scholar

Hesmondhalgh, David. 2010. User-generated content, free labour and the cultural industries. Ephemera Theory & Politics in Organization 10(3/4). 267–284.Search in Google Scholar

Jenkins, Henry. 2006. Interactive audiences? The “Collective Intelligence” of media fans. Fans, bloggers, and gamers, 134–151. New York: New York University Press.Search in Google Scholar

Jenner, Mareike. 2018. Netflix and the re-invention of television, 1st edn. Cham: Springer International Publishing.10.1007/978-3-319-94316-9_1Search in Google Scholar

Kaplan, Saul. 2014. The business model innovation factory. New York: John Wiley & Sons, Inc.Search in Google Scholar

Keane, Michael. 2016. Internet+ China: Unleashing the innovative nation strategy. International Journal of Cultural and Creative Industries 3(2). 68–74.10.4337/9781782549864Search in Google Scholar

Keane, Michael, Yu Haiqing, Zhao Elaine & Leong Susan. 2022. China’s digital presence in the Asia-Pacific. London: Anthem Press.Search in Google Scholar

Keltie, Emma. 2017. The culture industry and participatory audiences. Cham, Gewerbestrasse, Switzerland: Springer International Publishing AG, Springer International Publishing AG, Palgrave Macmillan.Search in Google Scholar

Kim, Jaekyeong, Choi Ilyoung & Li Qinglong. 2021. Customer satisfaction of recommender system: Examining accuracy and diversity in several types of recommendation approaches. Sustainability (Basel, Switzerland) 13(11). 1–21. https://doi.org/10.3390/su13116165.Search in Google Scholar

Lifestyle Asia. 2021. “WeTV always more 2022” is where your next level of entertainment experience awaits. Lifestyle Asia Bangkok. Available at: https://www.lifestyleasia.com/bk/culture/entertainment/wetv-always-more-2022-is-where-your-next-level-of-entertainment-experience-awaits/.Search in Google Scholar

Lin, Lisa. 2022. Streaming screen forms and aesthetics: Tencent video. Convergent Chinese television industries. Palgrave global media policy and business, 127–144. Cham: Palgrave Macmillan.10.1007/978-3-030-91756-2_6Search in Google Scholar

Liu, Yanna, Liu Shuang & Zhang Xuejing. 2015. 中美付费视频网站之比较: 用户, 内容, 与模式 [Comparison of paid-video websites between China and the United States: Users, content and mode]. Journal of China University of Geosciences (Social Science Edition) 15(6). 128–136. https://doi.org/10.16493/j.cnki.42-1627/c.2015.86.016.Search in Google Scholar

Lobato, Ramon. 2019. Netflix nations: The geography of digital distribution. New York: NYU Press.10.18574/nyu/9781479882281.001.0001Search in Google Scholar

Lobato, Ramon & Amanda Lotz. 2021. Beyond streaming wars: Rethinking competition in video services. Media Industries 8(1). 89–108.10.3998/mij.1338Search in Google Scholar

López García, Juan, Lizcano David, Ramos Celia & Matos Nelson. 2019. Digital marketing actions that achieve a better attraction and loyalty of users: An analytical study. Future Internet 11(6). 130–145. https://doi.org/10.3390/fi11060130.Search in Google Scholar

Lotz, Amanda. 2021. Media disrupted: Surviving pirates, cannibals, and streaming wars. Cambridge, Massachusetts: MIT Press.10.7551/mitpress/13868.001.0001Search in Google Scholar

Lotz, Amanda. 2022. Netflix and streaming video: The business of subscriber-funded video on demand. Cambridge, UK: Polity Press.Search in Google Scholar

Marciano, Alain, Nicita Antonio & Ramello Giovanni. 2020. Big data and big techs: Understanding the value of information in platform capitalism. European Journal of Law and Economics 50(3). 345–358. https://doi.org/10.1007/s10657-020-09675-1.Search in Google Scholar

MarketLine. 2011. Blockbuster Inc. SWOT analysis: A progressive digital Media business. London: Datamonitor Plc.Search in Google Scholar

Marr, Bernard. 2015. Big Data: Using SMART Big Data; analytics and metrics to make better decisions and improve performance. New York: Wiley.Search in Google Scholar

Moro Visconti, Roberto, Larocca Alberto & Marconi Michele. 2017. Big data-driven value chains and digital platforms: From value co-creation to monetization. Big data analytics: Tools and technology for effective planning, 355–371. New York: Chapman and Hall/CRC.10.1201/b21822-16Search in Google Scholar

Mulla, Tausif. 2022. Assessing the factors influencing the adoption of over-the-top streaming platforms: A literature review from 2007 to 2021. Telematics and Informatics 69. 101797. https://doi.org/10.1016/j.tele.2022.101797.Search in Google Scholar

Murray, Simone. 2016. Media convergence’s third wave: Content streaming. Convergence (London, England) 9(1). 8–18. https://doi.org/10.1177/135485650300900102.Search in Google Scholar

Netflix. 2022. Netflix basic with ads plan. Netflix Help Center. Available at: https://help.netflix.com/en/node/126831#:∼:text=Some%20movies%20and%20TV%20shows.Search in Google Scholar

Netflix Inc. 2021. Annual report pursuant to section 13 or 15(d) of the securities exchange act of 1934. Washington, D.C.Search in Google Scholar

Nieborg, David & Thomas Poell. 2018. The platformization of cultural production: Theorizing the contingent cultural commodity. New Media & Society 20(11). 4275–4292. https://doi.org/10.1177/1461444818769694.Search in Google Scholar

Noam, Eli. 2021. The technology, business, and economics of streaming video: The next generation of media emerges. Cheltenham: Edward Elgar Publishing Limited.10.4337/9781800375048Search in Google Scholar

Oba, Goro & Sylvia Chan-Olmsted. 2005. The development of cable television in East Asian countries: A comparative analysis of determinants. Gazette (Leiden, Netherlands) 67(3). 211–237. https://doi.org/10.1177/0016549205052225.Search in Google Scholar

Peters, Greg. 2022. Netflix starting from $6.99 a month. About Netflix. https://about.netflix.com/en/news/announcing-basic-with-ads-us.Search in Google Scholar

Petraetis, Greg. 2017. How Netflix built a house of cards with big data. InsiderPro. Available at: https://www.idginsiderpro.com/article/3207670/how-netflix-built-a-house-of-cards-with-big-data.html.Search in Google Scholar

Plothe, Theo & Amber Buck. 2019. Netflix at the nexus: Content, practice, and production in the age of streaming television. New York: Peter Lang Publishing, Inc.10.3726/b14725Search in Google Scholar

Ruby, Daniel. 2022. Netflix subscribers 2022—How many subscribers does Netflix have? Demandsage. Available at: https://www.demandsage.com/netflix-subscribers/.Search in Google Scholar

Schintler, Laurie & Connie McNeely. 2020. Encyclopedia of big data. Cham: Springer International Publishing.10.1007/978-3-319-32001-4Search in Google Scholar

Schuman, Evan. 2007. Online customers get some satisfaction. EWEEK. Available at: https://www.eweek.com/enterprise-apps/online-customers-get-some-satisfaction/.Search in Google Scholar

Shattuc, Jane. 2020. Netflix, Inc. and online television. A companion to television, 145–164. New York: John Wiley & Sons, Inc.10.1002/9781119269465.ch7Search in Google Scholar

Sherman, Zoe. 2020. Modern advertising and the market for audience attention: The US advertising industry’s turn-of-the-twentieth-century transition, 1st edn. London: Routledge.10.4324/9781315511573-1Search in Google Scholar

South China Morning Post. 2017. Tencent Video, iQiyi in race to lead China’s online video market. Advertising.scmp.com. Available at: https://advertising.scmp.com/latest-news/tencent-video-iqiyi-in-race-to-lead-chinas-online-video-market.Search in Google Scholar

Srnicek, Nick. 2016. Platform capitalism. Cambridge: Polity.Search in Google Scholar

Statista. 2023. Number of Netflix paid subscribers worldwide from 1st quarter 2013 to 3rd quarter 2022. Available at: https://www.statista.com/statistics/250934/quarterly-number-of-netflix-streaming-subscribers-worldwide/.Search in Google Scholar

Stuff. 2023. Thousands slam Netflix on social media over password sharing crackdown. Available at: https://www.stuff.co.nz/entertainment/tv-radio/300803154/thousands-slam-netflix-on-social-media-over-password-sharing-crackdown.Search in Google Scholar

TechCrunch. 2022. Netflix to introduce ad-supported plan over the next year or two. Available at: https://techcrunch.com/2022/04/19/netflix-to-introduce-ad-supporter-plan/.Search in Google Scholar

Tencent. 2015. 2011 aunnal report. Tencent Holdings Limited. Available at: https://doi.org/10.3987/contents-15-9104.Search in Google Scholar

Tencent. 2022a. Incorporated in the Cayman Islands with limited liability 2021 annual report. Available at: https://static.www.tencent.com/uploads/2022/04/07/7c31a327fb1c068906b70ba7ebede899.PDF.Search in Google Scholar

Tencent. 2022b. 2022 second quarter results presentation. Available at: https://static.www.tencent.com/uploads/2022/08/17/d0d74555dfe436e57a9a3c05a0ded87b.pdf.Search in Google Scholar

Tencent. 2022c. TENCENT ANNOUNCES 2022 THIRD QUARTER RESULTS. Available at: https://static.www.tencent.com/uploads/2022/11/16/33aad36dea97848eb75aa988d785e9f8.pdf.Search in Google Scholar

Tencent Video. 2021. 腾讯视频用户协议. [Tencent Video User Service Agreement]. M.v.qq.com. Available at: m.v.qq.com/about/privacy.html.Search in Google Scholar

The Wall Street Journal. 2022. The end of Netflix password sharing is nigh. WSJ. Available at: https://www.wsj.com/articles/netflix-password-sharing-end-11671636600.Search in Google Scholar

Turow, Joseph. 2013. How should we think about audience power in the digital age? The international encyclopedia of media studies, 2, 238–246. New York: John Wiley & Sons, Ltd..10.1002/9781444361506.wbiems999Search in Google Scholar

Wasko, Janet. 2018. Studying political economies of communication in the twenty-first century. Javnost 25(1–2). 233–239. https://doi.org/10.1080/13183222.2018.1424031.Search in Google Scholar

Wayne, Michael. 2021. Netflix audience data, streaming industry discourse, and the emerging realities of “popular” television. Media, Culture & Society 44(2). 1–17. https://doi.org/10.1177/01634437211022723.Search in Google Scholar

Weidhaas, Raphael, Schlögl Stephan, Halttunen Veikko & Spieß Teresa. 2021. Watch this! The influence of recommender systems and social factors on the content choices of streaming video on demand consumers. Lecture Notes in Information Systems and Organisation 47. 738–753. https://doi.org/10.1007/978-3-030-86797-3_49.Search in Google Scholar

Received: 2022-11-22

Accepted: 2023-02-19

Published Online: 2023-03-01

Published in Print: 2023-03-28

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/omgc-2022-0061

Keywords for this article

China; comparative studies; critical media industry research; Netflix; political economy; streaming media; Tencent Video; United States

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