Balancing Revenue Streams in Online Video Platforms: The Impact of Original Content Provision on Business Model Selection

Abstract

This research investigates the strategic decision related to original content provision and business model selection within the rapidly evolving online video industry. We develop a two-sided market model involving a video platform, users, and advertisers to analyze how the platform balances subscription and advertising revenue while offering original content. This study finds that the amount of original content provided directly influences market equilibrium, affecting the platform’s choice between a purely subscription-based model or a mixed model. The cost of original content production plays a critical role in this decision-making process. High production costs may lead the platform to favor a mixed model, offering less original content to generate both subscription and ad revenue. Lower costs, however, encourage a subscription model with more original content to boost subscribing revenue. Additionally, factors such as network externalities between users and advertisers also impact the business model choice. Importantly, the model shows that allowing free users limited access to original content can expand both subscriber and advertiser engagement, enabling platforms to enhance dual revenue streams without having to sacrifice one for the other.

1. Introduction

The online video industry has experienced rapid growth in recent years, fueled by widespread high-speed internet access and the increasing demand for on-demand, high-quality entertainment. According to Grand View Research, the global video streaming market was valued at USD 89.03 billion in 2022 and is projected to grow at a compound annual rate of 21.5% from 2023 to 2030. This explosive growth has intensified competition among platforms, forcing them to develop more effective strategies to attract and retain users in a crowded market.

Online video platforms can be broadly categorized into two types: short video platforms (e.g., TikTok, Kuaishou) and long video platforms (e.g., Netflix, iQIYI). While short video platforms emphasize user-generated content, algorithmic recommendations, and social interaction, long video platforms focus on professionally produced content such as movies, series, and documentaries. These platforms offer a more immersive viewing experience that is characterized by longer watching time and a higher user willingness to pay. The key competitive advantage of long video platforms lies in their ability to deliver exclusive, high-quality content, which is an increasingly central factor in attracting and retaining subscribers.

This paper focuses on the profitability models of online long video platforms. Users typically base their choice of these platforms on the quality and variety of video content [1]. Thus, in order to stay attractive to users, online video platforms strive to provide as much high-quality content as possible. Their thirst for content has driven up the content cost [2], leading to substantial fees for procuring third-party licensed videos. In response, platforms began to prioritize the development of their own original content to reduce the dependence on third-party content. Although investing in the production of original content may entail high initial costs, it is more cost-effective than third-party licensed content in the long run. On the one hand, platforms avoid paying additional copyright fees for original content. Once the original content is produced, it can be indefinitely streamed on the platform, avoiding issues such as removal off shelves or bidding wars triggered by copyright expiration. On the other hand, original content enhances user stickiness. As these contents are exclusively provided by the platform and users cannot access them from other platforms, it is necessary to continuously subscribe to the platform to track these contents.

Considering these factors, original content has become a major tool for online long video platforms to promote user subscriptions [3]. Prominent platforms like Netflix and iQIYI exemplify this trend. As early as 2013, Netflix made a substantial investment of USD 100 million to produce its first original series, “House of Cards”, and achieved a growth of over 2 million subscribers in the United States and 1 million subscribers in other regions within the first three months of its premiere [4]. Since then, original content has become the focus of Netflix. As of 2018, Netflix has produced over 1000 original titles. In the third quarter of 2022, thanks to the releases of original series “Stranger Things” and “Dahmer: Monster”, Netflix added more than 2.4 million subscribers, successfully reversing the trend of subscriber decline in the previous two quarters [5]. Netflix’s remarkable success with original content has drawn industry-wide attention, and other platforms have begun to increase their investment in original content to build their own content libraries. This includes iQIYI, China’s largest online video platform. Back in 2015, iQIYI announced plans to allocate half of its budget on new original content production with the aim of increasing its number of paying users [6]. By 2018, iQIYI’s investment in content development had reached RMB 21.1 billion (USD 3.13 billion), roughly equal to the total content expenditures of China’s six major broadcasting companies, and boasted over 250 original productions. In September 2022, the number of iQIYI’s subscribers exceeded 100 million [7]. This significant growth is largely attributable to its superior capability in producing original content [8].

Online video platforms’ original content is regarded as a model of high-quality dramas, characterized by high production values, edgy storytelling, and narrative complexity [9]. However, these benefits come with challenges. Producing original content at scale requires significant financial resources, and most platforms today invest far more in content creation than they recoup through subscriptions alone.

In practice, this pressure has led to divergent monetization strategies. Platforms like iQIYI have adopted mixed models, offering both free ad-supported access and premium ad-free subscriptions. Other platforms, such as Netflix and Amazon Prime, have opted for pure subscription models, eschewing advertising altogether to preserve a seamless user experience. These contrasting strategies raise a fundamental set of questions: Why do some platforms rely exclusively on subscription revenue while others pursue a combination of subscriptions and advertising? How do platforms balance content investment with user acquisition and monetization? Under what conditions is each model more profitable?

Although original content is widely seen as a key driver of subscription growth [10], most platforms still operate at a loss, relying heavily on debt financing to fund content production. Raising subscription fees may help close this gap, but such a move risks user churn. Alternatively, platforms can monetize their free user base through advertising, leveraging network externalities between users and advertisers. Yet, not all platforms pursue this dual strategy, and some consciously avoid advertising revenue despite rising content costs. This inconsistency highlights a gap in our understanding of how platforms make business model decisions under financial pressure.

Against this backdrop, our study aims to provide a systematic framework for understanding how online video platforms determine their original content strategies and select optimal business models. Specifically, we seek to answer the following questions:

• First, how should a platform determine the amount of original content to offer, and how should it price its subscription and advertising services?
• Second, under what conditions should a platform adopt a pure subscription model versus a mixed revenue model?
• Third, how do factors such as network externalities affect a platform’s decisions regarding its business model, content investment, and pricing?

To address these questions, we developed a formal model of a two-sided market consisting of a video platform, users, and advertisers. We show that the platform faces the following fundamental trade-off: increasing original content can boost subscription revenue but may reduce the number of free users and hence advertising income; conversely, scaling back original content can retain a larger free user base and increase advertising revenue but at the cost of reduced subscription income. A key finding is that the platform’s optimal business model depends on the marginal cost of content production; when costs are low, the platform prefers a subscription-only model with extensive original content; when costs are high, it shifts toward a mixed model with more limited content and greater reliance on advertising. The model also reveals how other market factors—such as the value original content creates for users and the benefit advertisers derive from user exposure—further influence this strategic choice.

This study makes three key contributions. First, it enriches the theory of two-sided markets. Previous studies on two-sided markets mainly focused on how network externalities impact platform pricing strategies, specifically how platforms develop pricing structures that subsidize users one side while charging the other side [11,12,13]. However, in contrast to traditional two-sided platforms, network externalities between users and advertisers on online video platforms show opposite directions. This special form of network externality implies that platforms cannot implement cross-subsidization strategies but must balance the interaction between users and advertisers. Hence, in addition to pricing decisions, online video platforms also need to decide on their business models. Our research explores in detail the impact of the opposite network externalities between users and advertisers on platform pricing strategies and business models. This attempt deepens and expands the application of two-sided market theory in the specific field of video platforms. Second, we further analyze the influence of network externalities on the content investment strategies of video platforms. Weeds [14] and Wu and Chiu [15] proposed that developing original content is an essential means for video platforms to compete for users. On the basis of their ideas, this study examines platforms’ content development strategies in the context of a two-sided market. We consider content investment as a non-pricing tool and systematically analyze its interaction with platforms’ pricing strategies, providing a theoretical basis for platforms on how to strategically use both the pricing and non-pricing tools to achieve optimal profits. Finally, this study provides new insights into the theory of platform business model choice. In this paper, for the first time, we analyze the connection between original content investment strategies and business models, and we find that different amounts of original content provided by platforms lead to different business models. Based on this, we deeply explore how market factors such as content value, investment costs, and advertising benefits shape the investment strategy of platforms, which in turn affects their choice of business model. This finding provides guidance for platforms on how to adjust and optimize their business models to accommodate different content strategies.

The remainder of this paper is organized as follows. Section 2 reviews the relevant literature and highlights the differences between our work and previous studies. The setup of our model is introduced in Section 3, and then in Section 4, we analyze the model and present the results. The baseline model is extended in Section 5. Finally, in Section 6, we summarize the main findings of the study, and describe managerial implications as well as the limitations of our research.

2. Literature Review

The online video market is characterized by platforms offering a variety of videos to attract viewers, while advertisers seek to promote their products and services to these viewers. In this sense, the online video market is a typical two-sided market. In two-sided markets, the platform acts as an intermediary to facilitate interaction between different user groups, where the benefits obtained by one group of users from joining the platform depend on the size of the other group of users [16]. This phenomenon, known as network externality, is a unique feature that distinguishes two-sided markets from traditional product markets [17]. The seminal studies on two-sided markets have emphasized the impact of network externalities between two user groups on platform pricing strategies [12,13,16,18,19,20], providing a theoretical foundation for analyzing these markets.

Unlike traditional two-sided markets, the two user groups in the online video market exhibit opposite network externalities. Empirical studies indicate that users on video platforms are usually not interested in advertisements, and reducing the number of advertisements is helpful for user growth [21]. On the contrary, the demand of advertisers shows a significant positive correlation with platforms’ user scale [22]. Given the opposite network externalities between users and advertisers, a core issue faced by online video platforms is how to adjust the supply of content and advertisements to balance these two user groups. Anderson and Coate [23] analyzed the relationship between the advertising supply of media platforms and the socially optimal level, and they gave solutions to market failure in the media industry. Peitz and Valletti [24] found that advertising intensity and content of programming are also related to the charging method in the media market. Advertising intensity is higher in the free market, and platforms under the paid model always maximally differentiate their content. Godes et al. [25] studied the impact of market competition on content pricing and found that media platforms may charge higher content prices in a duopoly than in a monopoly. Furthermore, many studies in the fields of marketing and operation management have also investigated content pricing and advertising provision strategies in media markets [26,27,28,29,30]. However, most of the above literature assumes that the platform provides the same content and advertisements to all users. In contrast, this study examines the increasingly popular practice of segmenting the user market through differentiated services in the media market, focusing on content and advertising strategies under different service modes of platforms.

The inherent heterogeneity among users in the online video market forms the basis for the platform to implement differentiated service strategies. Prasad et al. [31] conducted the first formal study on this phenomenon. In their research, there were two types of viewers in the market, those who are willing to pay a higher price and view fewer advertisements, or those wanting to pay a lower price but view more advertisements. By providing these two options to viewers, platforms can generate both advertising and subscription revenues. Prasad et al. confirmed that offering viewers differentiated price options is usually better than a pure advertiser-supported strategy or a pure pay-per-view strategy. Fan et al. [32] explored the optimal pricing and advertising strategies for media providers who simultaneously distribute content through online and traditional channels. In their model, the traditional channel is always free and ad-sponsored, while the online channel could adopt selling, ad-sponsored, or both strategies. They assumed that consumers are heterogeneous in their sensitivities to usage costs. Their main conclusion was that media providers should use the advertising strategy when online access cost is relatively high and sell programs online when this cost is low. However, it is always better for them to offer pricing and advertising options to consumers. Zennyo [33] studied whether it is necessary for platforms to introduce differentiated services, the difference being that he assumed that the cost of implementing such a strategy is non-negligible. Specifically, he examined two ad-supported platforms deciding whether to introduce an ad-free premium service in addition to the basic service with annoying advertisements. He found that the equilibrium business model choice depended on the fixed cost for introducing a premium service. When the fixed cost was low, adopting the freemium model was a dominant strategy for each platform. Conversely, when the cost was high, neither platform adopted the freemium model. If this fixed cost is at an intermediate level, asymmetric equilibrium where only one platform introduces the premium service may arise.

Among this stream of literature, the most relevant studies related to our research include Lin [34] and Carroni and Paolini [1]. Lin [34] analyzed the practice of differentiating ad allocations to segment consumers on media platforms. He mainly studied how the platform distributes ads across different consumer types and implements price discrimination in both consumer and advertiser markets. Our study is somewhat complementary to his research, as we explore the possibility of platforms differentiating users by providing different content. Carroni and Paolini [1] focused on two service strategies of streaming platforms, namely free basic services and paid premium services. In their research, the platform needed to decide on the quality upgrade level for paid services and consider whether to offer both services simultaneously, so as to choose among the free model, the subscription model, and the mixed model. They believed that users are annoyed by advertisements differently, and thus choose between the free basic service with ads and the ad-free premium service. In their model, users always generate revenue for the platform, as free users bring advertising revenue while paying users create subscription revenue. This leads to the scale of the user market becoming the determining factor for the platform’s business model choice. However, our research differs from Carroni and Paolini [1] in that we consider the heterogeneity in users’ valuation of platform content. In addition to choosing between free and premium services, users can also opt out of using the platform. In this context, the scale of the user market no longer has an important impact on the platform’s business model, and when making decisions, the platform needs to evaluate the potential losses caused by users who exit the market. In addition, our model takes into account a more generalized cost function faced by the platform when improving the quality level of premium services, and we provide a detailed analysis of how this cost influences the optimization decisions of the platform.

This study also adds to the literature on content provision of media platforms. For example, Chiang and Jhang-Li [2] examined two choices of digital content distribution for premium content providers, such as cable TV providers and streaming platforms. They discussed whether the content owner should keep the content exclusive throughout the contract period or redistribute the titles to the other platform after a certain delay. Their research revealed how content values would change the choice of the redistributor and the length of windowing delay between the two platforms. Amaldoss et al. [35] investigated media platforms’ content provision strategies and their implications for platforms’ profits and content suppliers’ profits, taking into account the cross-side network externalities of a multisided media market and the nature of competition in the content supplier market.

In this category of the literature, most relevant to our research are studies on platform content development, typically Weeds [14] and Wu and Chiu [15]. Weeds [14] considered two competing distributors in a pay TV industry, where one of the distributors had the ability to produce premium content and decide whether to license its premium content to a competitor in order to determine between exclusive and non-exclusive distribution. He found that non-exclusive distribution was always dominant in a static environment, but in a dynamic environment with switching costs, exclusive distribution conferred a market share advantage, benefiting the operator in the future, thereby becoming an effective strategy in the long run. Under a similar setting, Wu and Chiu [15] studied the impact of consumer multi-homing behavior on whether media platforms should develop new content themselves and the choice of exclusivity strategy for such content. They found that, in the absence of multi-homing, developing exclusive new content is an efficient competitive strategy for platforms but may worsen social welfare. However, in the presence of multi-homing, developing exclusive content is a dominant strategy that benefits platforms, consumers, and society.

Our study differs from the above two studies in the following two aspects. First, the studies by Weeds [14] and Wu and Chiu [15] only focused on the interaction between users and platforms, exploring how platform content development and distribution strategies affect user choice and platform profit. However, we construct a model in which a media platform interacts with two sides, users and advertisers, and we explore how network externalities between users and advertisers influence the platform’s content development and advertising strategies. Second, Weeds [14] and Wu and Chiu [15] focused on the paid business model of media platforms, while our study does not pre-assume how a platform earns profits, making the source of profits an endogenous outcome of the model. We consider that platforms offer users the choice of a free service with ads and an ad-free subscription service. Whether these two market segments exist at the same time depends on the content developed by the platform. We comprehensively analyze and compare the optimal amount of newly developed content and platform profits under different business models, focusing on how the platform selects the best business model according to the content development strategies.

Overall, while many previous studies have focused on how online video platforms segment users by offering various subscription options, most studies have failed to consider how platforms strategically decide on the quality differences between different services, nor have they conducted an in-depth analysis of the impact of the cost of implementing differentiated services on platform strategies. In our research, the formation of free and paid user market segments is driven by platforms’ decisions on how much original content to develop. Our research aims to reveal the connections between network externalities, original content, and business models, providing a new framework to explain the various business models manifested in the online video market. To the best of our knowledge, no scholars so far have explored the original content investment strategy of online video platforms in the context of two-sided markets, nor have they explained why, in dealing with the challenge of covering the production costs of original content, some platforms choose an ad-free subscription model while others adopt a strategy of introducing advertising. Our research attempts to fill this gap and provide a theoretical reference for the strategic choice of online video platforms.

3. Model

In this study, a monopolistic online video platform is considered to be a two-sided platform connecting users and advertisers. The platform caters to its users by providing video content, fulfilling their demands for information and entertainment, while simultaneously offering advertising space to advertisers, allowing them to promote their products or services to platform users. Next, we take a detailed look at these three market participants.

3.1. The Platform

The online video platform provides users with two service options, the free basic service and the paid premium service. In the free case, users can only watch the existing content of the platform and are frequently interrupted by advertisements during the viewing process. These advertisements create distractions for users, thereby reducing their utility. In the paying case, users pay a subscription fee in exchange for an upgraded and ad-free viewing experience. To encourage more users to pay for subscriptions, the video platform also invests in developing original content to increase the value offered to subscribers.

The video platform’s revenue comes from two sources, advertising fees paid by advertisers and subscription fees paid by users. Simultaneously, when investing in the production of original content, the platform also incurs certain development costs [9]. Higher levels of original content generally lead to increased development costs, which are consistent with the view in previous studies [17,36,37]. Thus, we assume that the total development cost of the platform is convex and increasing in the amount of original content $x$, given as $c{x}^2/2$. The parameter c represents the marginal development cost of the original content. Then, the profit of the video platform can be expressed as follows:

$$\pi =n_s{p}_s+n_a{p}_a-{\displaystyle \frac{1}{2}}c{x}^2$$

(1)

where $p_s$ and $p_a$ denote the subscription and advertising fees charged by the platform, respectively. $n_s$ represents the number of users subscribing to the premium service, and $n_a$ represents the number of advertisers participating on the platform, that is, the platform’s advertising intensity. In this setting, the online video platform faces the following two key decisions: (1) How much original content should it develop? (2) How should it determine the subscription price charged to users as well as the advertising intensity? In its attempt to maximize profit, the platform could pursue one of the following business models:

Subscription Model. The platform does not provide free services, and users must pay a subscription price to watch all the content on the platform. In this case, due to the absence of free users viewing advertisements, advertisers will not place ads on the platform, implying $n_a=0$ and $p_s>0$. The platform chooses to earn all its profits from users when pursuing this model.

Mixed model. The platform provides both free services and subscription services to users. Users choose whether to participate in the platform, and if so, they make a choice between the free and paid services, suggesting $0<n_a<1$ and $p_s>0$. When pursuing this model, the platform earns profits from both sides of the market.

3.2. Users

Imagine a user evaluating whether to engage with an online video platform. The platform offers the following two options: a free tier that includes advertisements and a paid subscription tier that removes ads and provides a higher-quality viewing experience. In making this decision, the user considers the following two factors: (1) how appealing the content is, and (2) how much the presence of ads detracts from the experience.

To capture preference heterogeneity, we introduce a user-specific parameter $\theta \in [0, 1]$, representing individual interest in the platform’s free content. A higher $\theta$ implies greater enthusiasm for the available content. Depending on their $\theta$ value, users may choose among three options: abandoning the platform, using it for free with ads, or subscribing for an enhanced experience:

• If a user’s interest $\theta$ is very low, even free access cannot compensate for the disutility caused by ads, leading them to exit the platform.
• If $\theta$ is moderate, the user may tolerate ads and opt for free usage.
• If $\theta$ is sufficiently high, the user values the content enough to justify paying for an ad-free, premium experience.

Grounded in this intuition, we introduce the thresholds ${\theta }_f$ and ${\theta }_s$ as behavioral cutoffs:

• ${\theta }_f$: the minimum content preference required for a user to tolerate ads and stay on the platform.
• ${\theta }_s$: the level at which users are willing to convert from a free to a paid subscription to avoid ads and enhance quality.

The distribution of users is represented in Figure 1.

The utility derived by users from the free and subscription services is respectively given by the following:

$$u_f=\theta v-\gamma n_a$$

$$u_s=\theta \left(1+k\right)v+\beta x-p_s$$

where $v>0$ represents the intrinsic value of the platform’s free content. When users pay a subscription fee $p_s$ to upgrade to premium service, they can enjoy a higher intrinsic value given by $\left(1+k\right)v$, where $k$ represents the improvement level of the intrinsic value enjoyed by subscribed users. Specifically, subscribed users can watch smoother and clearer videos with superior audio quality, therefore experiencing a higher intrinsic value [38], as advertisements typically interrupt users’ watching process, thus having negative effects on users. The nuisance cost caused by each unit of advertisement to users is measured by $\gamma$. Since users must watch advertisements when choosing a free service, their utility is reduced by $\gamma n_a$ when the advertising intensity of the platform is $n_a$. However, if users subscribe to the premium service, then they can skip the advertisements, thus avoiding a loss of utility.

The platform is considered to have sufficient resources to develop new original content. In general, online video platforms possess extensive user behavior data, which can be leveraged to summarize user preferences and create original content based on the precise analysis of these preferences to better meet user needs, greatly reducing the trial and error cost of these original content. Therefore, original content is often of high value [9]. Based on this fact, we believe that users share the same value evaluation for original content and assume it to be $\beta$. When the platform invests in producing $x$ original content, it brings a utility increase of $\beta x$ for subscribed users.

It is worth noting that, in our model, $\theta$ reflects users’ heterogeneous preferences for third-party licensed content, which typically exhibits long-tail characteristics and substantial variation due to differences in taste, cultural background, and prevailing trends; hence, it is treated as an endogenous, distributed parameter. In contrast, $\beta$, the valuation of original content, is assumed to be exogenous and homogeneous, as platform-produced originals are often exclusive, heavily marketed, and perceived more uniformly across users. Empirical evidence also shows that user ratings for original content exhibit significantly lower variance, supporting this modeling simplification.

According to users’ utility function, ${\theta }_f$ and ${\theta }_s$ satisfy the following conditions:

• Users are indifferent between leaving and using the free tier at ${\theta }_f$: ${\theta }_{f}v-\gamma n_a=0⟹{\theta }_f={\displaystyle \frac{\gamma n_a}{v}}$;
• Users are indifferent between free and paid tiers at ${\theta }_s$: ${\theta }_s\left(1+k\right)v+\beta x-p_s={\theta }_{f}v-\gamma n_a{⟹\theta }_s={\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}$.

It can be observed that users in the interval $\left[{\theta }_s,1\right]$ will pay the platform to skip advertisements and subscribe to the original video content, and users in the interval $\left[{\theta }_f,{\theta }_s\right]$ will choose the free service and watch advertisements, while other users in the interval l $\left[0,{\theta }_f\right]$ will not join the platform. Then, the number of subscribed and free users on the platform can be expressed as follows:

$$n_s=1-{\theta }_s=1-{\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}$$

(2)

$$n_f={\theta }_s-{\theta }_f={\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a}{v}}$$

(3)

3.3. Advertisers

Advertisers seek to attract potential buyers to their products or services by placing advertisements on the video platform. The utility of advertisers is expressed as follows:

$$u_a=\alpha n_f-p_a-\eta$$

where $p_a$ represents the lump-sum advertising fee charged by the platform. $\alpha$ represents the marginal utility or return that an advertiser obtains from showing one unit of advertisement to a user. It can be interpreted as the value density of exposure; a higher $\alpha$ indicates that the ad is more effective (e.g., higher click-through or conversion rate) and that the advertiser perceives a greater return per impression. Conversely, a low $\alpha$ suggests weak ad performance and a lower incentive for advertisers to invest in the platform. Since only free users watch advertisements, advertisers can only reach $n_f$ potential buyers through the platform and obtain a potential revenue of $\alpha n_f$.

Advertisers also incur a fixed cost $\eta$ in the process of designing and producing advertisements. We assume that advertisers are heterogeneous with respect to $\eta$, where $\eta$ satisfies a uniform distribution over the interval $\left[\mathrm{0,1}\right]$. Consistent with previous research [28], we assume that each advertiser can only place one advertisement on the platform. An advertiser decides to place an advertisement on the platform only when the net utility is non-negative, $u_a\ge 0$. Therefore, all advertisers with fixed costs $\eta \le \alpha n_f-p_a$ will pay $p_a$ to join the platform, and the demand function for advertising space is $\alpha n_f-p_a$. We refer to the number of advertisements displayed on the platform as the advertising intensity and set it as a decision variable of the platform. When the platform decides to provide $n_a$ advertising space to advertisers, the fee clearing the market, that is, the advertising fee that makes the demand for advertising space equal to its supply, can be expressed as follows:

$$p_a=\alpha n_f-n_a=\alpha \left({\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a}{v}}\right)-n_a$$

(4)

The decision process of our model is as follows: In the first stage, the platform decides how much original content $x$ to develop. In the second stage, the platform simultaneously sets the advertising intensity $n_a$ and the subscription price $p_s$. Finally, in the third stage, after observing the amount of original content and prices of the platform, advertisers decide whether to place advertisements on the platform, and users decide whether to join the platform and then whether to subscribe to the premium service.

4. Results

In this section, we use backward induction to solve the model. We first focus on the optimal subscription price and advertising intensity in the second stage. Then, based on these expected results, we analyze how the platform decides its investment strategy for original content and selects the appropriate business model to maximize profits in the first stage.

4.1. Stage 2: Subscription Price and Advertising Intensity

Let us assume that, in the first stage, the amount of original content developed by the platform is $x$. Now, it decides how to maximize profits by choosing the advertising intensity $n_a$ and the subscription price $p_s$. In particular, by substituting Equations (2)–(4) into the platform’s profit function (1), we can express the profit maximization problem as follows:

$$\underset{p_s,n_a}{\max }\pi =n_s{p}_s+n_a{p}_a-{\displaystyle \frac{1}{2}}c{x}^2=p_s\left(1-{\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}\right)+n_a\left[\alpha \left({\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a}{v}}\right)-n_a\right]-{\displaystyle \frac{1}{2}}c{x}^2$$

$$\mathrm{s}.\mathrm{t}. 0\le n_a\le 1$$

$$\mathrm{s}.\mathrm{t}. {\displaystyle \frac{\gamma n_a}{v}}\le {\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}\le 1$$

$$\mathrm{s}.\mathrm{t}. \alpha \left({\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a}{v}}\right)-n_a\ge 0$$

In the above maximization problem, the platform’s profit takes into account the money raised by subscriptions as well as the advertising revenues, minus the costs incurred in developing original content. The advertising intensity $n_a$ and the subscription price $p_s$ are the platform’s decision variables at this stage. The constraints on these two decision variables are necessary for the platform to have both non-empty sets of paid users and free users. Intuitively, the first constraint ensures that the platform’s advertising intensity must be non-negative and cannot exceed the maximum demand of advertisers. The second constraint is to ensure that both free and paid user segments can be formed. It can be seen from this constraint that, if the subscription price is too high, so that the condition ${\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}>1$ is satisfied, users will only pay for the premium service when their evaluation $\theta >1$. However, the maximum value of $\theta$ does not exceed 1, so there will be no paying users. When the subscription price is too low and meets the condition ${\displaystyle \frac{p_s-\beta x-\gamma n_a}{kv}}<{\displaystyle \frac{\gamma n_a}{v}}$, the indifference points mentioned above will satisfy ${\theta }_s<{\theta }_f$. As a result, the utility of users choosing to subscribe to the premium service is always higher than the utility of choosing free service, so all users will choose paid subscriptions, and there will be no free users. Finally, the third constraint ensures that the advertising fees charged by the platform are always positive, otherwise introducing advertisements will not bring any revenue to the platform. For the convenience of subsequent analysis, we believe that the conditions stated in Assumption 1 are held throughout this paper.

Assumption 1.

$2<\gamma <\alpha$, $v<4\alpha \gamma /\left(\alpha +\gamma -4\right)$. Moreover, $2\left(v+\alpha \gamma \right)k-\alpha \left(\alpha -\gamma \right)>0$.

Firstly, we assume that the nuisance cost caused by advertisements to users, $\gamma$, is lower than the marginal revenue that users create for advertisers, $\alpha$. If not, the disutility caused by each ad exceeds its value to advertisers, leading to user churn and reduced advertising revenue. In such cases, the platform’s advertising model fails to generate profit.

In order to be closer to the actual situation, we assume that the existing video content on the platform often fails to satisfy diverse user preferences, especially as premium, exclusive offerings become key competitive differentiators. In practice, platforms may lack the licensing rights or financial means to acquire high-demand content, much of which is locked in exclusivity deals. As competition intensifies, users increasingly seek platforms with unique, original content. Thus, relying solely on standard free content limits user retention and engagement, justifying platforms’ strategic pivot toward original content and subscription models.

Finally, the parameter $k$ reflects the added value users perceive in upgrading to a subscription (e.g., ad-free viewing, higher quality, exclusive content). The condition $2\left(v+\alpha \gamma \right)k-\alpha \left(\alpha -\gamma \right)>0$ ensures that subscriptions provide sufficient utility to offset the loss in advertising revenue. A higher $\alpha$ raises the threshold for $k$, requiring more value in subscriptions to justify giving up ad revenue. Conversely, higher $v$ or $\gamma$ values (i.e., weak free content or high ad annoyance) lower the required $k$, as fewer users remain on the free tier. Moreover, this condition guarantees the existence of a unique interior equilibrium and supports the model’s theoretical stability by capturing the dynamic balance between the two revenue streams.

Now, we can solve the profit maximization problem and summarize the results in the following lemma.

Lemma 1.

Let $\overline{x}={\displaystyle \frac{v\left(\alpha -\gamma \right)\left(kv+k\alpha \gamma +\alpha \gamma \right)}{\beta \left(\alpha +\gamma \right)\left(v+\alpha \gamma \right)}}$. The optimal advertising intensity and subscription price of the online video platform have the following two cases:

• If $x>\overline{x}$, then $n_a=0$, the platform only provides the paid premium service and opts for a purely subscription-based model. At this time, the platform sets the optimal subscription price as ${\widehat{p}}_s^{*}={\displaystyle \frac{\beta x+kv+v}{2}}$.
• If $x<\overline{x}$, the platforms offers both the free service and the premium service. In this case, the platform adopts a mixed model. The subscription price is $p_s^{*}$, and the advertising intensity is $n_a^{*}$, where $n_a^{*}={\displaystyle \frac{\left(\alpha +\gamma \right)kv-\left(\alpha -\gamma \right)\beta x}{4k\left(v+\alpha \gamma \right)-{\left(\alpha +\gamma \right)}^2}}$, $p_s^{*}={\displaystyle \frac{\beta \left(2kv+2k\alpha \gamma +\alpha \gamma -{\alpha }^2\right)x+2kv\left(kv+k\alpha \gamma +\alpha \gamma \right)}{4k\left(v+\alpha \gamma \right)-{\left(\alpha +\gamma \right)}^2}}$, and $p_a^{*}={\displaystyle \frac{\beta \left(\alpha +\gamma \right)\left(v+\alpha \gamma \right)x-v\left(\alpha -\gamma \right)\left(\alpha \gamma +kv+k\alpha \gamma \right)}{4kv\left(v+\alpha \gamma \right)-{v\left(\alpha +\gamma \right)}^2}}$.

Lemma 1 elaborates the platform’s optimal strategy for advertising intensity and subscription price in the second stage. It can be seen that the amount of original content provided by the platform in the first stage endogenously determines the business model it adopts. When there is less original content available, the platform tends to choose a mixed model, obtaining both advertising revenue and subscription revenue. On the contrary, when the amount of original content is large enough, the platform is more inclined to abandon advertising and rely entirely on user subscriptions for profit. The reason behind this is that, as the amount of original content increases, the platform is able to create higher added value for subscribers, which greatly enhances users’ willingness to pay for a subscription. As a result, more and more free users will switch to the premium service under the drive of original content. As the number of free users declines, the revenue of advertisers gradually decreases, causing more and more advertisers to leave the market because they cannot break even. The platform has to lower the advertising prices to maintain advertiser participation. In the extreme case, when the amount of original content increases to a certain threshold $\overline{x}$, the advertising fee drops to 0. At this point, the platform will lose the incentive to offer the free service and, thus, transition to a purely subscription-based model.

Lemma 2.

Let $\underset{\_}{x}={\displaystyle \frac{\left(\alpha v+\gamma v-4v-4\alpha \gamma \right)k+{\left(\alpha -\gamma \right)}^2}{\beta \left(\alpha -\gamma \right)}}$, where $\underset{\_}{x}<\overline{x}$. If the advertising intensity threshold $L=\left(\alpha v+\gamma v-4v-4\alpha \gamma \right)k+{\left(\alpha -\gamma \right)}^2>0$, when $x<\underset{\_}{x}$, although the platform still adopts a mixed model, it always maintains the maximum advertising intensity $n_a=1$ and sets the subscription price as ${\tilde{p}}_s^{*}={\displaystyle \frac{\beta x+kv+\alpha +\gamma }{2}}$. The advertising price becomes ${\tilde{p}}_a^{*}={\displaystyle \frac{\alpha \left(\alpha -\gamma \right)+k\left(\alpha v-2\alpha \gamma -2v\right)k-\alpha \beta x}{2kv}}$.

As mentioned in Lemma 1, when the amount of original content provided by the platform is limited, a mixed business model is more favorable. Under this model, the platform needs to make a trade-off between subscription revenue and advertising revenue. As the amount of original content continues to decline, the number of subscription users and the subscription price decrease correspondingly, leading to a reduction in platform subscription revenue. Instead, the number of free users rises, which in turn helps the platform to attract more advertisers. Hence, the platform will consider increasing advertising intensity to expand advertising revenue. We define $L=\left(\alpha v+\gamma v-4\alpha \gamma -4v\right)k+{\left(\alpha -\gamma \right)}^2<0$ as the advertising intensity threshold and find that, under the condition of $L>0$, when the amount of original content decreases to $\underset{\_}{x}$, the demand of advertisers will reach its maximum. Although, with the further reduction of original content, the scale of free users continues to expand, and the revenue brought to advertisers continues to rise, but the demand of advertisers cannot be further expanded. In this case, the platform will not continue to increase the supply of advertising space but will keep the advertising intensity equal to the maximum demand of advertisers and raise the advertising fee to ${\tilde{p}}_a^{*}$ to achieve profit growth.

However, it is not always the case that advertisers’ demand reaches its maximum. When the advertising intensity threshold $L$ is lower than 0, even if the amount of original content is reduced to 0, the demand of advertisers cannot achieve the maximum. Specifically, the markets that meet the above condition can be summarized into the following two types: (1) $v<2\alpha \gamma /\left(\alpha -2\right)$, or (2) $v>2\alpha \gamma /\left(\alpha -2\right)$, but $k>{\left(\alpha -\gamma \right)}^2/\left(4\alpha \gamma +4v-\alpha v-\gamma v\right)$. In the first type of market, the intrinsic value provided by the platform’s existing video content to users is relatively low, leading to fewer users choosing free services of the platform. In the second type, although the existing video content provides higher intrinsic value to free users, the intrinsic value enjoyed by subscription users is significantly enhanced compared to free users. Therefore, regardless of original content, the enhancement of intrinsic value can drive enough users to switch from free services to paid subscriptions, resulting in a limited number of users choosing free services. In both situations, due to the smaller size of free users, advertisers are not able to earn sufficient revenue from participating in the platform, making advertisers with high fixed costs unable to break even and consequently exist in the market. Only in the market where $v>2\alpha \gamma /\left(\alpha -2\right)$ and $k<{\left(\alpha -\gamma \right)}^2/\left(4\alpha \gamma +4v-\alpha v-\gamma v\right)$ is the platform allowed to generate high enough advertising benefits for advertisers through a sufficient number of free users to attract the participation of all advertisers. Since $L=\left(\alpha v+\gamma v-4v-4\alpha \gamma \right)k+{\left(\alpha -\gamma \right)}^2$ has an important impact on the platform’s advertising intensity decision, we define $L$ as the advertising intensity threshold in the subsequent analysis.

Combining the conclusion in Lemma 1, we find that, when the platform adopts the mixed model, it can choose to implement two strategies. In the first strategy, the platform provides relatively more original content to gain higher subscription revenue. At this time, users are more inclined to subscribe to premium services, and fewer users choose free services; thus, the platform can only achieve a moderate level of advertising intensity and correspondingly obtain lower advertising revenue. In contrast, in the second strategy, the platform is only willing to provide limited original content, placing greater emphasis on advertising revenue. Due to the fewer number of paid users, the platform gets lower subscription revenue. However, because of the huge size of free users, the platform can achieve maximum advertising intensity and further increase advertising revenue by charging higher advertising fees than the first strategy (i.e., ${\tilde{p}}_a^{*}>p_a^{*}$). It is worth noting that the second strategy is only available when the advertising intensity threshold $L$ is positive. Otherwise, when $L$ is negative, the platform only has the option of moderate advertising intensity strategy under the mixed model.

4.2. Stage 1: Original Content

In the first stage, the platform decides how much original content to provide in order to maximize the total profit. The platform’s profit is expressed as the sum of subscription revenue and advertising revenue minus the production cost of original content, where the form of cost function is $c{x}^2/2$. To focus on the interior solution, we assume that the marginal production cost of original content $c$ satisfies the condition that $c>c_0$, where $c_0=\alpha {\beta }^2\left(v+\alpha \gamma \right)/\left[v\left(\alpha -\gamma \right)\left(kv+k\alpha \gamma +\alpha \gamma \right)\right]$. Based on this assumption, we can derive the optimal amount of original content under different models and express the result in the following lemma.

Lemma 3.

The optimal strategy for providing original content has the following three cases:

• Under a subscription model, the optimal amount of original content is ${\widehat{x}}^{*}=\overline{x}$.
• Under a mixed model, if the platform implements the maximum advertising intensity strategy, the optimal amount of original content is ${\tilde{x}}^{*}={\displaystyle \frac{\beta \left(kv-\alpha +\gamma \right)}{2ckv-{\beta }^2}}$.
• Under a mixed model, if the platform implements the moderate advertising intensity strategy, the optimal amount of original content is $x^{*}={\displaystyle \frac{v\beta \left[2k\left(v+\alpha \gamma \right)-\alpha \left(\alpha -\gamma \right)\right]}{c\left[4kv\left(v+\alpha \gamma \right)-{v\left(\alpha +\gamma \right)}^2\right]-2{\beta }^2\left(v+\alpha \gamma \right)}}$.

Indeed, in order to make users willing to pay subscription fees, the platform needs to provide sufficient original content. This ensures that users obtain enough utility growth from the premium service to offset the costs associated with subscribing. Consequently, the amount of original content under the purely subscription-based model, ${\widehat{x}}^{*}$, is always higher than the amount of original content under the mixed model, ${\tilde{x}}^{*}$ and $x^{*}$. However, in adopting this business model, although increasing the amount of original content can continuously raise the number of subscribers and subscription prices, it also expands the cost of producing original content. Additionally, the growth of subscription revenue cannot completely offset these additional production costs. Therefore, the platform tends to provide the minimum amount of original content for implementing this model, that is $\overline{x}$ stated in Lemma 1. It is worth noting that $\overline{x}$ is only affected by users’ utility and is independent of the platform’s cost of producing original content.

Conversely, if the platform adopts a mixed model, then the amount of original content reflects the utility difference between the free and paid options. More original content will incentivize users to switch from the free services to paid subscriptions. At this point, the platform needs to strike a balance between the following two decisions. On the one hand, by increasing the amount of original content, the platform can expand the number of subscribers and raise the subscription price, thereby enhancing subscription revenue. On the other hand, the platform can also increase the advertising intensity and obtain higher advertising revenue by reducing the amount of original content. Under the mixed model, the choice of original content is closely related to its production cost. Regardless of the strategy of advertising intensity, the optimal amounts of original content, ${\tilde{x}}^{*}$ and $x^{*}$, both decrease with the production cost $c$. This suggests that, if the cost of creating original content is low, then the subscription revenue generated by the increase in original content will be sufficient to cover the production cost, prompting the platform to provide more original content. On the contrary, if the production cost is high, producing more original content will exacerbate the cost burden of the platform. In this case, the platform will reduce the original content to alleviate the cost pressure, compensating for the loss in subscriptions by increasing advertising intensity.

However, under the condition where advertising intensity threshold $L>0$, if the platform continues to reduce original content, the demand of advertisers will reach the maximum. Beyond this point, further reducing original content cannot stimulate more advertising demand. In this case, the production cost of original content becomes the key determinant of whether the platform adopts the strategy of maximum advertising intensity or that of moderate advertising intensity. Let $\overline{c}={\beta }^2\left(\alpha v-2\alpha \gamma -2v\right)/\left[\left(\alpha v+\gamma v-4v-4\alpha \gamma \right)k+{\left(\alpha -\gamma \right)}^2\right]$, we find that when $c>\overline{c}$, constrained by the higher production costs, the platform is more inclined to provide less original content and set maximum advertising intensity to maximize profit. Conversely, when $c<\overline{c}$, the production cost of original content is relatively low, and the subscription revenue brings an appropriate increase in the original content that is enough to make up for the production cost and increase the profit. Therefore, the platform will increase the original content and reduce the advertising intensity, thus switching to the moderate advertising intensity strategy. As mentioned before, when the advertising intensity threshold $L<0$, the platform cannot provide sufficient advertising benefits to attract all advertisers, leaving the moderate advertising intensity strategy as the only viable option.

The platform’s optimal profit should be denoted when choosing the subscription model as ${\pi }_S^{*}$. Under the mixed model, if the platform opts for maximum advertising intensity, the optimal profit is denoted as ${\tilde{\pi }}_M^{*}$, while the optimal profit when choosing moderate advertising intensity is ${\pi }_M^{*}$. $\Delta \tilde{\pi }$ and $\Delta \pi$ are used to represent the differences between optimal profits, where $\Delta \tilde{\pi }={\pi }_S^{*}-{\tilde{\pi }}_M^{*}$ and $\Delta \pi ={\pi }_S^{*}-{\pi }_M^{*}$. We find that $\mathsf{\partial }\Delta \tilde{\pi }/\mathsf{\partial }c<0$ and $\mathsf{\partial }\Delta \pi /\mathsf{\partial }c<0$; thus, there exist ${\tilde{c}}_{SM}$ and $c_{SM}$ that make ${\pi }_S^{*}={\tilde{\pi }}_M^{*}$ and ${\pi }_S^{*}={\pi }_M^{*}$ hold, respectively. ${\tilde{c}}_{SM}$ and $c_{SM}$ also satisfy $c_0<c_{SM}$ and $c_0<{\tilde{c}}_{SM}$ and, thus, meet the assumption. In the previous analysis, we pointed out that the amount of original content will endogenously determine the business model chosen by the platform. However, the original content provided by the platform is primarily constrained by the production costs, thus making these costs a crucial factor influencing the platform’s choice of business model. The following propositions provide a systematic overview of the pathways of this influence.

Proposition 1.

In the case of $L<0$, if $c<c_{SM}$, the platform only offers the premium service and chooses the subscription model. If $c>c_{SM}$, the platform offers a menu of free and premium services, chooses the mixed model, and implements the strategy of moderate advertising intensity.

According to the previous analysis, when $L<0$, the platform can only achieve moderate advertising intensity under the mixed model. In this case, the platform needs to decide the amount of original content to provide, which will further determine whether to choose the subscription model or the mixed model with moderate advertising intensity.

In our model, the benefits that advertisers derive from participating in the platform entirely come from the network externalities contributed by free users. The platform is unable to offer additional value to advertisers, but for users, not only can existing video content on the platform provide intrinsic value, but original content also generates additional value. As a result, the platform can always charge users a higher subscription price compared to the advertising fee charged to advertisers. In the ad-free subscription model, the platform attracts more users to pay for a subscription by providing more original content. In contrast, under the mixed model, the platform sacrifices some subscription users in exchange for more advertisers. Since the subscription price is always higher than the advertising price, when the cost of producing original content is low, the subscription model with more paying users always creates higher profits than the mixed model. However, as the production cost $c$ increases, since the optimal original content in the subscription model, ${\widehat{x}}^{*}$, always stays at the value $\overline{x}$, the total cost of providing original content continually rises with $c$. This increase gradually offsets the advantage brought by the higher subscription price, leading to a decline in platform profits. When $c$ increases to $c_{SM}$, the optimal profits under the subscription model drop to be equal to the optimal profit under the mixed model, that is, ${\pi }_S^{*}={\pi }_M^{*}$. Although, as $c$ increases, the profits in the mixed model also decrease, and the platform will correspondingly reduce the original content provided, thus slowing down the decline rate of profit. Therefore, when $c$ continues to increase beyond $c_{SM}$, ${\pi }_S^{*}$ will be lower than ${\pi }_M^{*}$, and the platform will choose the mixed model and set moderate advertising intensity.

We then further illustrate the above results utilizing a numerical example. By setting $\alpha =5$, $\gamma =3$, $\beta =4$, $v=5$, and $k=1$, we obtain the results shown in Figure 2. Under these conditions, we can calculate that the advertising intensity threshold $L$ is lower than 0; thus, the platform is not able to achieve the maximum demand of advertisers. From the left-hand side of Figure 2, we can observe that, when the marginal production cost of original content is below a threshold value, $c_{SM}$ (approximately 8.1436 given our selected parameters), the optimal profits in a subscription model consistently surpass those in a mixed model. Thus, when $c$ remains below this threshold, the platform always chooses the subscription model to optimize profits. As $c$ becomes larger, a decrease in the optimal profits in both models is observed. However, the rate of decline in the subscription model exceeds that of the mixed model. The right-hand side of Figure 2 shows that, when $c$ increases above 8.1436, the mixed model’s optimal profit outperforms that of the subscription model. Consequently, the platform transitions to the mixed model to maximize profits.

Proposition 2.

In the case of $L>0$:

• If the improvement level of intrinsic value for subscribed users,$k$, is greater than $\overline{k}$, when $c<c_{SM}$, the platform only offers the premium service and chooses the subscription model; when $c_{SM}<c<\overline{c}$, the platform offers a menu of free and premium services, chooses the mixed model and implements the strategy of moderate advertising intensity; when $c>\overline{c}$, the platform chooses the mixed model with maximum advertising intensity.
• However, if $k<\overline{k}$, when $c<{\tilde{c}}_{SM}$, the platform chooses the subscription model; otherwise, when $c>{\tilde{c}}_{SM}$, the platform chooses the mixed model and implements the strategy of maximum advertising intensity.

When $L>0$, the platform has the potential to achieve the maximum intensity of advertising under the mixed model. In this case, the platform needs to choose between an ad-free subscription model, a mixed model with moderate advertising intensity, and a mixed model with maximum advertising intensity. Among these three models, the interaction of two elements (the cost of producing content ($c$) and the users’ willingness to pay for an improved experience ($k$)) determines which revenue model maximizes the platform’s payoff under different conditions. This is described more specifically as follows:

Pure subscription equilibrium. This equilibrium arises when users place high value on the subscription service (i.e., $k$ is large), and the platform faces relatively low content production costs (i.e., $c$ is small). In this case, the platform optimally adopts a pure subscription model, removing all ads and offering a premium, paid-only experience. In this setting, users are willing to pay for an ad-free, high-quality environment; at the same time, the low content production cost enables the platform to achieve considerable revenue without relying on ad revenue to recoup content costs. The focus shifts to enhancing user retention through superior original content. This model is suitable for platforms that have cost advantages and users that are willing to pay for high-quality content.

Hybrid equilibrium with moderate ad intensity. When users still exhibit strong willingness to pay (high $k$), but content production costs are moderate, the platform moves toward a mixed strategy, which means supplementing subscription income with moderate advertising. At this time, although subscription revenue has potential, it is insufficient to fully offset content production cost; thus, the platform will supplement revenue through a moderate amount of advertising. In this model, the platform usually provides both free and paid services; free users need to watch a certain amount of advertisements, and paid users enjoy an ad-free experience. This strategy can not only expand user coverage, but it can also balance the relationship between advertising revenue and subscription revenue, avoiding significant damage to user experience due to excessive advertising. Moderate advertising intensity helps maintain the stability of the platform’s revenue while maintaining high user satisfaction and usage stickiness. Therefore, the mixed model with medium advertising intensity is well-suited for platforms aiming to expand reach while maintaining subscription incentives and content investment.

Hybrid equilibrium with maximum ad intensity. In contrast, when users’ perceived value of subscription is relatively low (i.e., $k$ falls below a certain threshold), or when content production costs are high (large $c$), the platform is incentivized to rely more heavily on advertising as its primary revenue source. In this case, the platform cannot rely on subscription revenue alone to cover costs, either because users are unwilling to pay or the investment in content is too costly to be supported by subscriptions. Consequently, the platform maximizes ad placements—even at the risk of some user churn—to increase revenue through higher ad impressions and conversion. This model is more viable for platforms serving cost-sensitive or less-engaged user segments, especially when users’ tolerance for advertising is relatively high.

In order to better illustrate the above results, we present a numerical study in Figure 3. We set $\alpha =5$, $\gamma =3$, $\beta =4$, and $v=14$ in this example, and these parameters yield a threshold $k$ of approximately 0.35, i.e., $\overline{k}=0.35$ in this case. By setting $k=0.7$ in the left panel and $k=0.3$ in the right panel, we can use the left and right panels of Figure 3 to represent the cases of $k>\overline{k}$ and $k<\overline{k}$, respectively. Then, we plot how the platform’s optimal profits under different business models change with the production cost of original content in these two cases. From the left panel of Figure 3, we observe that when $k>\overline{k}$, the critical values of the marginal production cost of original content satisfies $c_{SM}<\overline{c}$. Additionally, when $c$ is lower than $c_{SM}$, the optimal profit in the subscription model, ${\pi }_S^{*}$, consistently surpasses those in the mixed models, ${\tilde{\pi }}_M^{*}$ and ${\pi }_M^{*}$. Thus, if $c$ falls below $c_{SM}$, it is better for the platform to choose the subscription model. However, if $c$ increases from $c_{SM}$ to $\overline{c}$, then the optimal profit in the moderate-advertising-intensity mixed model, ${\pi }_M^{*}$, overtakes ${\pi }_S^{*}$, becoming the maximum profit under the three business models. In such a scenario, the platform should adopt the mixed model with moderate advertising intensity when $c$ lies in the interval $\left[c_{SM},\overline{c}\right]$ to maximize profits. Further, when $c$ exceeds $\overline{c}$, the optimal profit in the maximum-advertising-intensity mixed model, ${\tilde{\pi }}_M^{*}$, turns to be the maximum value among the three optimal profits. This shift prompts the platform to switch to the mixed model with maximum advertising intensity to obtain the largest revenue.

The right panel of Figure 3 illustrates the case where $k<\overline{k}$. In this situation, the critical values of the marginal production cost now align to $\overline{c}<{\tilde{c}}_{SM}$. We can observe that the optimal profit ${\pi }_M^{*}$ is higher than ${\tilde{\pi }}_M^{*}$ when $c<\overline{c}$, and it becomes lower than ${\tilde{\pi }}_M^{*}$ when $c>\overline{c}$. However, as long as $c$ remains below ${\tilde{c}}_{SM}$, these two optimal profits under the mixed model, ${\pi }_M^{*}$ and ${\tilde{\pi }}_M^{*}$, consistently stay beneath the optimal profit in the subscription model, ${\pi }_S^{*}$; thus, the platform always chooses the subscription model to optimize profits when $c<{\tilde{c}}_{SM}$. When $c$ surpasses ${\tilde{c}}_{SM}$, ${\tilde{\pi }}_M^{*}$ exceeds ${\pi }_S^{*}$, becoming the largest profit obtained by the platform under the three different business models. As a result, the platform transitions to the mixed model with maximum advertising intensity to maximize profits.

After discussing the strategic choice of the platform in equilibrium, we further investigate the influence of market characteristics on the platform’s choice of business model and obtain the following findings.

Proposition 3.

As the marginal revenue $\alpha$ contributed by users to advertisers increases, the platform earns less profits under a subscription model but more profits under a mixed model, eventually switching from a subscription model to a mixed model.

This proposition describes the influence of network externalities that users contribute to advertisers on platform profits and the business model. As $\alpha$ increases, advertisers are willing to pay more for participating in the platform, and the potential advertising revenue of the platform increases accordingly. This change makes the mixed model that introduces advertising more attractive relative to the ad-free subscription model. Thus, when $\alpha$ is large, the platform will switch from the ad-free subscription model to the mixed model to earn both advertising and subscribing revenue. This strategic shift assists in increasing the platform’s profits. To understand this phenomenon, first recall that, in Lemma 1, we find that, when the market-clearing advertising fee drops to zero, the platform loses its motivation to attract advertisers by offering free services and thus shifts to a purely subscription-based model. However, advertisers’ revenue increases when $\alpha$ increases, allowing the platform to charge higher advertising fees; thus, it has enough incentive to choose the mixed model. With the growth of $\alpha$, the platform will only enable the subscription model when it further increases the supply of original content to reduce the number of free users, so that the advertising fee that advertisers are willing to pay is reduced to zero. Although increasing the amount of original content allows the platform to charge users more, due to the higher costs of producing original content, the growth in subscription revenue is insufficient to offset these costs, which eventually leads to a decline in platform profits. Secondly, under the mixed model, as $\alpha$ increases, the platform will reduce investment in original content to boost the number of free users, thereby attracting more advertisers and collecting higher advertising fees. In this scenario, the platform’s advertising revenue increases, while the cost associated with providing original content decreases due to its reduced supply, contributing to an improvement of overall profits.

We use Figure 4 to further illustrate the changing trend of optimal profits under the subscription model and the mixed model with respect to $\alpha$. In order to meet the requirements in our assumptions and ensure the existence of equilibrium, we set the parameters as $\beta =2$, $\gamma =3$, $v=13.5$, $k=0.55$, and $c=0.7$. More importantly, we combine the two situations of moderate and maximum advertising intensity under the mixed model, and the optimal profit ${\mathsf{\pi }}_M^{*}$ is designated as ${\pi }_M^{*}$ when $c$ is lower than $\overline{c}$ and as ${\tilde{\pi }}_M^{*}$ when $c$ is higher than $\overline{c}$. From Figure 4, we can see that, when $\mathsf{\alpha }$ is low, the subscription model brings the highest profit to the platform, so the platform should choose the subscription model for profit maximization. As $\alpha$ increases, the optimal profit under the subscription model gradually decreases, while the optimal profit under the mixed model keeps increasing. Eventually, at sufficiently high values of $\alpha$, the optimal profit in the mixed model surpasses the profit in the subscription model, causing the platform to shift to the mixed model to maximize profit.

Proposition 4.

As the marginal value $\beta$ of original content to users increases, the platform switches from a mixed model to a subscription model and earns more profits.

To understand this proposition, notice that the marginal value $\beta$ that original content delivers to users also reflects users’ desire for original content. As $\beta$ increases, users are willing to pay higher subscription fees and are more inclined to subscribe to the platform’s original content, gradually making the strategy of introducing advertising less attractive. With the growth of $\beta$, an increasing number of users opt for subscription services, and the platform finally shifts from a mixed model to a subscription model with large $\beta$. Under the mixed model, as $\beta$ increases, the platform increases the supply of original content. This move helps to substantially enlarge the utility of subscribing users, enabling the platform to charge higher subscription fees. Such a price increase can significantly expand the platform’s profit, effectively offsetting the increased cost of providing original content. At the same time, with the increase of $\beta$, a large number of free users convert to subscribing users, resulting in a continuous decrease in the revenue of advertisers as well as the market-clearing advertising fees. Therefore, under the subscription model, the amount of original content that the platform needs to provide decreases with the increase of $\beta$. Such adjustments save costs for the platform and improve overall profits.

Figure 5 provides a more intuitive illustration of how optimal profits vary with respect to $\beta$ in both the subscription and mixed models. The parameter settings are that $\alpha =5$, $\gamma =3$, $v=14$, $k=0.7$, and $c=4$ in Figure 5. The expression of the optimal profit under the mixed model follows a pattern similar to that depicted in Figure 4. It can be seen from Figure 5 that, as $\beta$ grows, the optimal profit in both the subscription model and the mixed model keeps increasing. Additionally, when $\beta$ is low, the mixed model yields the maximum profit for the platform. However, when $\beta$ becomes sufficiently large, the optimal profit of the subscription model outperforms that of the mixed model. This observation suggests that, as $\beta$ increases, the platform should shift from a mixed model to a subscription model to optimize profit.

5. Extension

In our baseline model above, we assume that the online video platform only provides original content to its subscribers. In this section, we consider an alternative assumption that the platform also allows the free users to watch a few portion of its original content. We discuss how the platform’s investment in original content and strategic choice of business model are affected under this assumption.

Consider a case where free users are allowed to watch an amount of original content equal to $\eta x$, where $0<\eta <1$. For the ease of subsequent analysis, we assume that the proportion of original content accessible to free users meets the condition $\eta <\mathrm{m}\mathrm{i}\mathrm{n}\left\{\left(v+\alpha \gamma \right)/\left(v+\alpha \gamma +{\alpha }^2\right),\left(2v+2\alpha \gamma -\alpha v\right)/\left(2v+2{\alpha }^2-\alpha v\right)\right\}$. This assumption ensures that the amount of original content available to free users is limited, thus keeping the subscription service attractive to users. Otherwise, if $\eta$ is too high, the original content available to free users will be almost the same as that available to subscribers, which will greatly weaken the appeal of the subscription service, thus reducing users’ willingness to pay for subscriptions. In this case, the demand of subscribing and free users are as follows:

$$n_s=1-{\displaystyle \frac{p_s-\beta \left(1-\eta \right)x-\gamma n_a}{kv}}$$

$$n_f={\displaystyle \frac{p_s-\beta \left(1-\eta \right)x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a-\beta \eta x}{v}}$$

The platform’s profit maximization problem now becomes the following:

$$\begin{array}{l}\underset{p_s,n_a}{\max }\pi =n_s{p}_s+n_a{p}_a-{\displaystyle \frac{1}{2}}c{x}^2\\ =p_s\left(1-{\displaystyle \frac{p_s-\beta \left(1-\eta \right)x-\gamma n_a}{kv}}\right)+n_a\left[\alpha \left({\displaystyle \frac{p_s-\beta \left(1-\eta \right)x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a-\beta \eta x}{v}}\right)-n_a\right]\\ -{\displaystyle \frac{1}{2}}c{x}^2\end{array}$$

$$\mathrm{s}.\mathrm{t}. 0\le n_a\le 1$$

$$\mathrm{s}.\mathrm{t}. {\displaystyle \frac{\gamma n_a-\beta \eta x}{v}}\le {\displaystyle \frac{p_s-\beta \left(1-\eta \right)x-\gamma n_a}{kv}}\le 1$$

$$\mathrm{s}.\mathrm{t}. \alpha \left({\displaystyle \frac{p_s-\beta \left(1-\eta \right)x-\gamma n_a}{kv}}-{\displaystyle \frac{\gamma n_a-\beta \eta x}{v}}\right)-n_a\ge 0$$

Similar to the baseline model, constraints on the two decision variables, subscription price and advertising intensity, are critical to ensure the existence of equilibrium. Violation of these constraints could potentially prevent the coexistence of both subscribing and free users. The first constraint implies that the platform’s advertising intensity is non-negative and does not exceed the maximum demand generated by advertisers. The second constraint ensures that, even when users can view original content with a percentage of $\eta$ for free, a segmented market of free and paying users can still be formed. The third constraint guarantees that the advertising fee charged by the platform is always non-negative.

The decision-making process of the platform remains a two-stage procedure, which includes deciding on the amount of original content to provide in the first stage and adjusting advertising intensity and subscription price to maximize profits in the second stage. Unlike the baseline model, we find that, when free users can watch some of the original content, the level of intrinsic value enhancement $k$ enjoyed by subscribing users plays a significant role in the platform’s choice of business model.

Lemma 4.

Let $\widehat{k}={\displaystyle \frac{\left(\alpha +\gamma \right)\left(v-\eta v+\alpha \gamma \right)-\alpha \gamma \left(v-\eta v+2\eta \alpha \right)}{\eta \alpha \left(2v+2\alpha \gamma -\gamma v\right)}}$, the market equilibrium has the following two possible outcomes in the second stage:

• When subscribing users enjoy a lower level of intrinsic value promotion, that is, $k<\widehat{k}$, the optimal advertising intensity and subscription price have the following two cases:

  (a) 

  If $x>{\displaystyle \frac{v\left(\alpha -\gamma \right)\left(kv+k\alpha \gamma +\alpha \gamma \right)}{\beta \left[\left(\alpha +\gamma \right)\left(v-\eta v+\alpha \gamma \right)-2\eta \alpha \left(v+\alpha \gamma \right)k-2\eta {\alpha }^2\gamma \right]}}$, then $n_a=0$, the platform only provides the paid premium service, chooses the subscription model, and sets the optimal subscription price as ${\widehat{p}}_s^{\prime *}={\displaystyle \frac{v+\beta x+kv}{2}}$.

  (b) 

  If $x<{\displaystyle \frac{v\left(\alpha -\gamma \right)\left(kv+k\alpha \gamma +\alpha \gamma \right)}{\beta \left[\left(\alpha +\gamma \right)\left(v-\eta v+\alpha \gamma \right)-2\eta \alpha \left(v+\alpha \gamma \right)k-2\eta {\alpha }^2\gamma \right]}}$, then the platform offers both free and premium services. In this case, the platform adopts the mixed model and sets the optimal advertising intensity and susception price as $n_a^{\prime *}$ and $p_s^{\prime *}$ respectively, where $p_s^{\prime *}={\displaystyle \frac{2v\left(v+\alpha \gamma \right)k^2-\left(1-\eta -k\eta \right)\left(\alpha -\gamma \right)\alpha \beta x+2\alpha \gamma k\left(v+\beta x\right)-2\left(1-\eta \right)kv\beta x}{4k\left(v+\alpha \gamma \right)-{\left(\alpha -\gamma \right)}^2}}$ and $n_a^{\prime *}={\displaystyle \frac{kv\left(\alpha +\gamma \right)+2\eta k\alpha \beta x-\left(1-\eta \right)\left(\alpha -\gamma \right)\beta x}{4k\left(v+\alpha \gamma \right)-{\left(\alpha -\gamma \right)}^2}}$.

• When subscribing users enjoy a higher level of intrinsic value promotion, that is, $k>\widehat{k}$, the platform always adopts a mixed model, and the optimal advertising intensity and subscription price have the following two cases:

  (a)

  If $x<{\displaystyle \frac{4k\left(v+\alpha \gamma \right)-kv\left(\alpha +\gamma \right)-{\left(\alpha -\gamma \right)}^2}{2\eta k\alpha -\left(1-\eta \right)\left(\alpha -\gamma \right)}}$, then the platform sets the optimal advertising intensity as $n_a^{\prime *}$ and the optimal susception price as $p_s^{\prime *}$.

  (b)

  If $x>{\displaystyle \frac{4k\left(v+\alpha \gamma \right)-kv\left(\alpha +\gamma \right)-{\left(\alpha -\gamma \right)}^2}{2\eta k\alpha -\left(1-\eta \right)\left(\alpha -\gamma \right)}}$, then the platform always maintains the maximum advertising intensity and sets the optimal subscription price as ${\tilde{p}}_s^{\prime *}={\displaystyle \frac{\alpha +\gamma +kv+\left(1-\eta \right)\beta x}{2}}$.

In the baseline model, owing to the platform’s policy of not providing original content to its free users, the only way for these users to access such content is through subscription. As the platform expands its supply of original content, an increasing number of free users choose to upgrade to subscriptions, resulting in an increase in platform subscription revenue. Meanwhile, as the number of subscribers increases, the number of free users decreases correspondingly. This reduction undermines the platform’s appeal to advertisers, subsequently leading to a decrease in advertising revenue. Therefore, the platform must strike a balance between investing in original content to boost subscription revenue and reducing the supply of original content to maintain advertising revenue. Interestingly, if the platform offers a small amount of original content to its free users, then it has the opportunity to increase the supply of original content to attract more subscribers, while simultaneously increasing the number of free users. In this scenario, the platform has the potential to achieve the dual growth of subscription revenue and advertising revenue without any trade-off between them.

The reason is that as $x$ increases, the utility of subscribing users will continue to grow, consequently resulting in a steady rise in their number. However, the utility of free users is impacted by two contrasting trends. On the one hand, the increase of $x$ will also enhance the utility of free users and attract potential users who have not participated in the platform before to join the platform and enjoy free services. This trend is conducive to the growth of free users. On the other hand, since the proportion of original content available for subscribers is always higher than that of free users, the utility growth of subscribers will significantly exceed that of free users as $x$ increases, driving some free users to upgrade to a paid subscription. This trend will lead to a decline in the number of free users. Which of these two opposite trends dominates depends on the level of intrinsic value enhancement enjoyed by the platform’s subscribing users.

When the platform offers a lower level of intrinsic value to its subscribers, i.e., when $k<\widehat{k}$, the difference between the intrinsic value enjoyed by subscribers and free users is not significant. In this case, increasing the amount of original content can help widen the utility gap between subscribers and free users, which in turn promotes more free users to upgrade to subscriptions. In this context, the loss of free users will be more pronounced as $x$ increases. Consequently, with lower $k$, the number of free users will decrease as $x$ increases. However, when $k$ is relatively high, i.e., $k>\widehat{k}$, the platform’s improvement of intrinsic value has already resulted in a substantial increase in the utility of subscribers, and the utility growth brought about by adding more original content becomes not significant at this time. In this case, an increase in original content results in only a small number of free users transitioning to paid subscriptions. However, since an increase in $x$ will attract more potential users to use the platform’s free service, the number of free users will grow.

Therefore, when $k<\widehat{k}$, as $x$ increases, the number of free users gradually declines, consequently leading to a reduction in advertisers’ revenue as well as their willingness to pay for advertising. The platform needs to reduce advertising fees accordingly to ensure the participation of advertisers. When $x$ increases to a certain threshold, advertising fees fall to zero, and the platform no longer has the incentive to provide advertising space. It will then stop offering free services and transition to a subscription model. On the contrary, when $k>\widehat{k}$, as $x$ increases, the number of free users increases, and the revenue of advertisers rises correspondingly. Advertisers are then willing to pay higher advertising fees; thus, the platform has sufficient motivation to provide advertising space and will adopt a mixed model. As the amount of original content continues to expand, once advertisers’ demand reaches its peak, the platform will be unable to further attract the participation of advertisers. At this point, the platform will maintain the maximum advertising intensity and maximize profits by raising the market clearing price for advertising.

Based on the above results, we can derive the optimal amount of original content provided by the platform and the corresponding profits under different models in the first stage. Let ${\pi }_S^{\prime *}$, ${\pi }_M^{\prime *}$, and ${\tilde{\pi }}_M^{\prime *}$ denote the platform’s optimal profits under the subscription model, the mixed model with moderate advertising intensity, and the mixed model with maximum advertising intensity, respectively. We find that within the feasible range of marginal production cost of original content, there exists $c_{SM}^{\prime }$ that enables the platform to obtain the same optimal profit under the subscription model and the mixed model with moderate advertising intensity; that is, $c_{SM}^{\prime }$ makes ${\pi }_S^{\prime *}={\pi }_M^{\prime *}$ hold. Additionally, there exists $c^{\prime }$ that makes the platform obtain the same profit under different choices of advertising intensity in the mixed model, i.e., ${\pi }_M^{\prime *}={\tilde{\pi }}_M^{\prime *}$. By comparing these optimal results, we have the following conclusion.

Proposition 5.

When users can access a limited amount of original content for free, the choice of the business model for the platform is as follows:

• When the level of intrinsic value enhancement experienced by subscribing users meets the condition $k<\widehat{k}$, if the cost of producing original content is less than $c_{SM}^{\prime }$, i.e., $c<c_{SM}^{\prime }$, the platform should choose the subscription model. Conversely, if $c>c_{SM}^{\prime }$, a mixed model with moderate advertising intensity is preferred.
• When the level of intrinsic value enhancement experienced by subscribing users meets the condition $k>\widehat{k}$, if the cost of producing original content is less than $c^{\prime }$, i.e., $c<c^{\prime }$, the platform should adopt the mixed model with maximum advertising intensity. Conversely, if $c>c^{\prime }$, the platform should choose the mixed model with moderate advertising intensity.

When free users can access a limited amount of original content, the platform’s choice of business model is also endogenously influenced by the production decision of original content. As the platform is constrained by costs when making its production decisions, these costs become a key factor in determining the business model.

Firstly, according to the results of Lemma 4, when $k<\widehat{k}$, as the amount of original content increases, the platform transitions from a mixed model to a subscription model. When the marginal cost of producing original content is low, the platform tends to provide a larger amount of original content and opts for a subscription model. This model allows the platform to achieve a larger scale of a subscriber base and to charge higher subscription prices to compensate for the increased cost in producing original content. However, if the marginal cost is high, then offering more original content will significantly increase the platform’s total costs. Even if the subscription revenue is able to grow with more original content, it is difficult to offset such a substantial cost burden. Therefore, when faced with a higher marginal cost, the platform aims to reduce the supply of original content to save costs. The decrease in the original content will lead the platform to turn to a mixed model, attracting advertisers to participate and generating a certain amount of advertising revenue to further compensate for the high cost of producing original content.

We use Figure 6 to present the above results in an intuitive way. By setting the parameters $\alpha =4$, $\gamma =3$, $\beta =4$, $v=10$, and $\eta =0.3$, the threshold value $\widehat{k}$ is approximately 1.2. In order to illustrate the case of $k<\widehat{k}$, we assign $k=0.3$ in Figure 6. It can be seen that when $c$ is low, the subscription model initially yields higher profits than a mixed model with moderate advertising intensity. As $c$ increases, the optimal profit in both models decreases. Once $c$ increases to $c_{SM}^{\prime }$, the platform makes the same profits under both models. Then, if $c$ continues to rise and exceeds $c_{SM}^{\prime }$, the optimal profit under the subscription model will be overtaken by the optimal profit under the mixed model. Therefore, to maximize profits, the platform should opt for a subscription model when $c<c_{SM}^{\prime }$ and shift to a mixed model with moderate advertising intensity when $c>c_{SM}^{\prime }$.

Moreover, when $k>\widehat{k}$, with the increase of original content, the platform consistently adopts a mixed model but gradually increases advertising intensity until it maintains the maximum. In this case, as the original content increases, the numbers of subscribing users and subscribing prices rise accordingly. Meanwhile, when $k>\widehat{k}$, the number of free users also grows with the increase in original content, bringing a higher revenue to advertisers, and the platform is able to attract more advertisers to participate. When the number of advertisers increases to a peak, it can further increase revenue by raising advertising prices. Therefore, when the marginal cost of producing original content is low, the platform always offers a large amount of original content to achieve maximum advertising intensity. Due to the lower marginal cost, the total cost of producing more original content is relatively acceptable, and the platform can offset these costs through higher subscription prices and advertising fees. However, when the marginal cost is high, the platform will bear a heavy cost burden when providing more original content. Therefore, it hopes to offer less original content to reduce costs, and this eventually leads the platform to choose the mixed model of moderate advertising intensity.

We also use a numerical example to illustrate the above result. Given that $\widehat{k}$ is approximately 1.2, we set $k=2$ in Figure 7 to exemplify the case of $k>\widehat{k}$. As Figure 7 shows, when $c$ is lower than $c^{\prime }$, the strategy employing maximum advertising intensity always brings the largest profit to the platform adopting the mixed model. As $c$ increases, the optimal profit obtained through this strategy experiences a significant decrease, and when $c$ reaches $c^{\prime }$, it decreases to be equal to the optimal profit under the moderate advertising intensity strategy. As $c$ continues to grow and exceed $c^{\prime }$, the strategy of moderate advertising intensity begins to yield the highest profit for the platform. Therefore, the choice of strategy for the platform should be adaptive, transitioning from maximum to moderate advertising intensity as $c$ surpasses $c^{\prime }$.

6. Conclusions and Implication

The video industry is undergoing dramatic changes. Cable TV used to be the only channel for people to access video content. However, with the rapid development of the Internet, traditional cable TV providers are losing market share, and online video platforms have emerged as the preferred mode for people to watch videos. To compete for limited audience attention, an increasing number of platforms are introducing exclusive original content. Although the original content brings high value to the audience, the platforms have to invest heavily in the production of this content. Therefore, finding a cost-effective strategy for offering original content is crucial for these platforms. Furthermore, the Internet-based video service provides platforms with multiple possible business models. Online video platforms also face the critical question of how to choose the optimal business model to balance the high costs of producing original content. Despite the significant boom in the online video market over the past decade, these newly emerged issues have not been effectively addressed. The purpose of this study is to provide a theoretical analysis of these issues, offering guidance for the operational decision of online video platforms.

6.1. Conclusions

In this study, we constructed a two-sided market model consisting of online video platforms, users, and advertisers. We examined how a platform determines the amount of original content to provide to balance the subscription revenue and advertising revenue. By comprehensively considering the influence of multiple factors such as the production cost of original content and the opposite network externalities between users and advertisers, we also explored the platform’s strategic choice between a subscription model and a mixed model.

Firstly, we analyzed the platform’s optimal strategy for providing original content. The results indicate that the amount of original content provided will lead to a different market equilibrium. When the platform provides a large amount of original content, the number of subscribing users will significantly increase, while the number of free users dramatically decreases, resulting in the market size of free users being insufficient to incentivize advertisers to pay for advertisements. In this situation, we find that the platform lacks motivation to attract advertiser participation; thus, it will abandon the advertising market and choose the subscription model. However, the platform can segment the user market by providing less original content. Under this strategy, the platform can not only maintain a certain scale of subscribing users to obtain subscription revenue, but it can also retain enough free users to attract advertisers and generate advertising revenue, thus forming a market equilibrium of the mixed model. Additionally, we find that, in the market with an advertising intensity threshold $L>0$, the platform has the opportunity to further increase the scale of free users by continuously reducing the supply of original content, so as to attract all advertiser participation and maintain the maximum advertising intensity.

Subsequently, we analyzed the factors influencing the platform’s choice of business model. The above analysis indicates that the amount of original content provided by the platform will endogenously determine the business model it chooses. Since the production cost of original content determines the platform’s content investment strategy, this makes cost a key factor affecting the platform’s business model. Specifically, if market conditions are unfavorable for the platform to achieve maximum advertising intensity (i.e., $L<0$), the platform facing a higher production cost tends to offer less original content and choose a mixed model, so as to simultaneously obtain subscription and advertising revenue to offset the high cost of original content. On the contrary, if the production cost is low, then the platform is more inclined to provide a large amount of original content and adopt a subscription model, which would increase profits by charging a higher subscription price. However, when $L>0$, the platform needs to decide the appropriate advertising intensity when implementing the mixed model. Our research found that, if the platform provides a higher degree of intrinsic value enhancement for subscribers (i.e., $k>\overline{k}$), as the cost $c$ increases, the platform’s willingness to provide original content decreases. It will gradually reduce the supply of original content while increasing advertising intensity, transitioning from a purely subscription-based model to a mixed model with moderate advertising intensity, and it will eventually switch to maximum advertising intensity when the cost is high enough. However, we find that if $k<\overline{k}$, then the mixed model with moderate advertising intensity will never become the dominant strategy for the platform. In this case, as $c$ increases, the platform will move directly from a subscription model to a maximum advertising intensity model. Furthermore, besides the cost coefficient, we find that when the marginal revenue $\alpha$ that users bring to advertisers increases, or the marginal value $\beta$ that original content brings to users decreases, the platform will also transition from a subscription model to a mixed model.

Lastly, we explored an alternative scenario, where the platform allows free users to access some of the original content, and how this will affect its business model and content investment decisions. In this setting, we observed that the platform has an opportunity to expand the supply of original content to attract more subscribers while also increasing the number of free users. This mechanism results in the platform consistently choosing the mixed model and being able to achieve the dual growth of subscription and advertising revenue without having to trade-off between them.

6.2. Managerial Implication

The findings of this study have important practical implications. First of all, we have observed tremendous changes in the video industry over the past two decades. In the past, video content was primarily broadcast via cable TV, and viewers could only watch certain TV shows on a fixed schedule. Nowadays, with the rapid development of the Internet, online video platforms such as Netflix, Amazon Prime, and iQIYI have gradually become the main channels for video content distribution. These platforms utilize streaming technology to offer video services, allowing users to watch videos simultaneously while downloading, eliminating the need to wait for the entire video file to be downloaded. The popularity of smartphones and tablets also allows users to watch videos anywhere and anytime. In addition, platforms can also produce original content that is more appealing to its users, based on their viewing history and habits. These transformations have profoundly changed the way video content is consumed, with an increasing number of users willing to pay for online video services. With the rapid growth in the number of paying users [39], online video platforms thus need to reconsider whether to continue offering free services.

This research can provide support for platforms in making such critical decisions. We found that, when the amount of original content provided by an online video platform reaches a certain scale, users are more inclined to pay for subscriptions, so that the scale of free users is not enough to support the platform to obtain advertising revenue. This will lead the platform to stop providing free services. However, if the amount of original content is relatively small, the number of subscribing users is limited, and the subscription revenue generated by the platform cannot cover the production costs of original content, necessitating the introduction of advertising to increase revenue. Therefore, platforms with a lower cost of producing original content tend to provide a large amount of original content to choose a subscription model, while those facing higher production costs have to reduce the supply of original content to choose a mixed model. In addition to the production cost, the quality of original content, which is specifically reflected as the marginal value created for users by original content, is also an important factor influencing the platform’s business model. As this value increases, the platform will transition from a mixed model to a subscription model. Therefore, when choosing a business model, online video service providers need to fully consider the quality of the original content they provide and their production costs of this content.

In practice, we can observe that, although both iQIYI and Netflix are committed to the development of original content, in terms of actual output, Netflix is significantly ahead of iQIYI in both quantity and quality. This is mainly due to the fact that the video industry started earlier in the United States, and the infrastructure and producing environment for video production are relatively superior. As the world’s leading online video platform, Netflix has rich experience in content production. In addition, the production scale of its content is typically large, and it also has a global production network. These advantages enable Netflix to allocate resources more effectively, realize economies of scale, and reduce its marginal cost of producing original content. As a result, Netflix is able to produce a large amount of original content with relatively limited funds, and this rich original content brings a higher subscription revenue to Netflix, enabling it to abandon advertising and choose a subscription model.

Moreover, Netflix is also at the forefront in terms of technology and innovation, particularly its in-depth investment in the fields of AI algorithms and data science. This allows it to use vast amounts of user behavior data to produce and optimize its original content in order to better meet the needs and preferences of a broad user base. Therefore, Netflix’s original content can provide higher value to users, and this high-quality original content is an essential driver for Netflix to choose a purely subscription-based model. In contrast, due to lack of production scale and experience, the cost of producing original content for iQIYI is relatively high. Although iQIYI also invests heavily in original content every year, the quantity and quality of its output are relatively low. Thus, it is difficult to obtain sufficient subscription revenue, and iQIYI have to opt for a mixed model, introducing advertising to increase income. These observations further validate our findings and confirm the important impact of the production cost and quality of original content on platform business model choices.

In addition to examining factors related to original content, the platform also needs to pay attention to the marginal revenue rate of advertisers, i.e., the marginal value contributed by free users to advertisers, during their decision-making process of the business model. According to the prediction of our model, the platform will choose a mixed model only when the revenue rate of advertisers is relatively high. If advertisers can only achieve a lower revenue rate from advertising on the platform, the platform will switch to a subscription model. In recent years, as ad-blocking technology has become more advanced, it has become increasingly difficult for advertisers to reach consumers, leading to a gradual decrease in the effectiveness of online advertising. This may be one of the reasons for the increasing number of platforms choosing the subscription model.

Additionally, we also found that, if the platform provides a small amount of original content to free users, there is an opportunity to increase the number of free users and subscribed users at the same time. Therefore, the platform needs to pay special attention to the market parameters that lead to this situation and provide the corresponding proportion of original content to achieve a win–win situation of simultaneous growth in subscription revenue and advertising revenue.

6.3. Limitation and Future Direction

Our research also has several limitations. First, this study regards the online video platform as a two-sided platform connecting users and advertisers, ignoring the existence of third-party content providers. Given that the investment cost of original content accounts for a significant proportion of the annual content expenses of online video platforms, this study considers the cost of purchasing third-party licensed videos as a sunk cost and thus neglects it, focusing primarily on the cost of investment in producing original content. Future research could consider introducing a new market side, namely the third-party content provider, constructing the online video platform as a multi-sided platform, and it could investigate how platforms balance decisions between investing in original content and purchasing licensed videos. Second, our research model was established in the context of a monopoly market. Future studies could explore how platforms determine their original content strategies and business models within a competitive environment. Finally, in the model extension part, we consider the proportion of original content that free users can view as a given exogenous variable. Future research could treat this as a decision variable of the platform.