Sponsored Data Services and Consumer Welfare on Mobile Broadband

Transcription

1 Association for Information Systems AIS Electronic Library (AISeL) ICIS 2017 Proceedings Dec 10th, 12:00 AM Sponsored Data Services and Consumer Welfare on Mobile Broadband Liangfei Qiu University of Florida, Chong (Alex) Wang Peking University Guanghua School of Management, Jia Jia Hong Kong University of Science and Technology, Follow this and additional works at: Qiu, Liangfei; Wang, Chong (Alex); and Jia, Jia, "Sponsored Data Services and Consumer Welfare on Mobile Broadband" (2017). ICIS 2017 Proceedings This material is brought to you by the International Conference on Information Systems (ICIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in ICIS 2017 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 Sponsored Data Services and Consumer Welfare on Mobile Broadband Completed Research Paper Liangfei Qiu Warrington College of Business, University of Florida Gainesville, FL, United States Chong (Alex) Wang 1 Guanghua School of Management, Peking University Beijing , China alexwang@gsm.pku.edu.cn Jia Jia Department of ISOM, HKUST Business School Clear Water Bay, Kowloon, Hong Kong justinjia@ust.hk Abstract Sponsored data services provide an alternative data consumption option for users of mobile broadband services. Data sponsoring programs allow content providers (CPs), rather than consumers, to pay for data traffic incurred when consuming the sponsoring CP s digital content. Internet service providers (ISPs) have been experimenting with this option for effective monetization of mobile broadband traffic. Meanwhile, policy makers are concerned about its market implications. In this study, we examine the impact of sponsored data services on an ISP s pricing decisions and consumer welfare in a stylized game-theoretical model. We allow consumer heterogeneity in both usage pattern and taste for content services. Our model analysis reveals that a profitmaximizing ISP will always, irrespective of the market condition, prefer the arrangement that allows both CPs to sponsor data traffic. However, we find that the ISP s decision to induce sponsoring competition on the CPs side will not help to increase consumer welfare. Keywords: Sponsored data service, Mobile broadband, Network neutrality, Consumer heterogeneity, Consumer welfare 1 Please send all correspondence to Chong (Alex) Wang (alexwang@gsm.pku.edu.cn). Thirty Eighth International Conference on Information Systems, Seoul

3 Introduction These free-data plans (sponsored data plans) have proven to be popular among consumers, particularly low-income Americans, and have enhanced competition in the wireless marketplace, new Federal Communications Commission Chairman Ajit Pai said in a statement. 2 Sponsored data services (and similar data-sponsoring programs) provide an alternative data consumption option for customers of mobile broadband services and a different delivery mode for content service providers. Under a data-sponsoring program, charges for eligible data (traffic caused by use of the sponsoring app) are billed directly to the sponsoring company, i.e., the content provider. Eligible data sessions are identified by a sponsored data icon on the end user s device and itemized separately on the invoice. The first data-sponsoring program was introduced by AT&T in January Other big ISPs followed suit. T-Mobile s Binge On program allowed subscribers to stream videos from Netflix and Amazon. Verizon announced a Free-Bee Data 360 program in January By allowing some content providers (CPs) to pay for data consumption on behalf of consumers, datasponsoring programs introduce a differentiation mechanism (e.g., Hong 2016; Cho et al. 2016). The U.S. telecommunications regulator, the Federal Communications Commission (FCC), raised concerns that such pricing schemes might have critical implications for the mobile information market and may alter the balance among content providers. As a response to the concerns, AT&T insisted that the sponsored data service only affects who pays for the (sponsored) traffic; it does not prioritize that traffic, nor does it throttle the usage of non-sponsored mobile destinations or content. Nevertheless, advocates of network neutrality are concerned about the truthfulness of such claims about network management. More importantly, it is unclear whether these innovative pricing schemes indeed benefit end users without reducing the freedom of the Internet by increasing the cost of running content services. Therefore, research is needed to help policy makers understand the impact of the new data-sponsoring programs on the market for mobile content services. In this study, we investigate the impact of the new data-sponsoring program in a stylized game-theoretical model to provide a better understanding of the impact of such programs on ISPs pricing decisions and consumer welfare. Specifically, we consider a broadband market in which a monopolist ISP provides mobile broadband access to Internet users and delivers services from two CPs to these users. Extending the literature on network neutrality, we consider mobile broadband users who are both horizontally and vertically differentiated: 1) they are heterogeneous in their data usage patterns and have different inherent request rates for data packets; and 2) they are heterogeneous in their tastes for the offerings of CPs and incur fit costs for mismatching. To reflect common practice, the ISP offers data plans with caps. Users pay a fixed fee for a fixed data allowance (the cap on the amount of data they can use without additional charges), plus a variable fee per unit of data after exceeding the cap. With this setup, we analyze the equilibrium in which the ISP exercise price discrimination by exploring nonlinear pricing and the datasponsorship program simultaneously in a two-sided market. Our analysis reveals the interplay between the ISP s existing instrument for price discrimination and the new data-sponsorship program. We also show the consequences for consumer welfare. Specifically, we are interested in answering the following research questions: 1) How does an arrangement between the ISP and CPs for a data sponsorship program (that allows none, some, or all CPs in the market to provide sponsored data services) affect ISP pricing and heterogeneous users choices of CPs? 2) Do data sponsorship options, which essentially switch the cost of data consumption from broadband users to CPs, indeed benefit these users (increasing consumer surplus)? We report several interesting findings. First, in a market where CPs are relatively comparable in their per-user revenue-generation capability, the profitmaximizing ISP will always prefer the arrangement that allows both CPs to sponsor their users. The data sponsoring program enables the ISP to extract profits from the CPs that are otherwise out of its reach, while the competition between CPs dictates that a larger portion of their revenues are transferred to the ISP. Second, under certain market conditions, CPs will opt-out of the sponsoring option. Data sponsoring programs are more attractive when the content market is less differentiated. Third, surprisingly, the ISP s decision to induce sponsoring competition on the CPs side does not benefit end users in general. Using a 2 (last accessed: September 5, 2017). Thirty Eighth International Conference on Information Systems, Seoul

4 nonlinear pricing scheme, the ISP can extract all consumer surplus except for the compensation for the fit costs of indifferent users. Such compensation is maximized when only one CP chooses to sponsor consumer data traffic. Literature and Background The discussion about data-sponsoring programs is rooted in the academic literature on network neutrality. In strict terms, network neutrality refers to the claim that Internet traffic should not be discriminated against in any form. Despite the purist proponents demand for strict network neutrality, which claims that all parts should be treated equally, FCC issued a Report and Order promulgating its network neutrality rules in As such, the status quo of industrial practice allows traffic management under the Best-Effort principle while emphasizing information transparency, no blocking of lawful content, and no unreasonable discrimination against content and content providers. To date, the debate between ISPs and the proponents of network neutrality has centered on the implications of two-sided pricing (e.g., termination fees and CP-tiering) for consumer welfare, competition, and innovation. Reviews of the economic literature on network neutrality can be found in Schuett (2010), Faulhaber (2011), and Krämer et al. (2013). Overall, the literature shows that the economic implications of termination fees depend on the market conditions and the scope of the analysis. For example, Economides and Tåg (2012) study termination fees in a two-sided market model and find that users and ISPs are better off if a lump-sum termination fee is allowed. Njoroge et al. (2014) further consider the ISP s investment decisions and find that a non-network neutrality regime leads to higher investment while the high-quality ISP prefers a network neutrality scenario. However, when multiple ISPs charge for access, the negative externality of one ISP overcharging for access results in network neutrality being a superior choice (Musacchio et al. 2009). A justification for termination fees and other forms of content tiering schemes is to avoid traffic congestion. Cheng et al. (2011) and Choi and Kim (2010) use a queuing model to conduct formal analyses of the effects of traffic priority on competition and welfare. When there is only a small difference in profit margins between the two CPs, they are both forced to buy the priority access and the price paid for priority is forfeited (Cheng et al. 2011). The analyses reveal that the ISP s incentive to invest in infrastructure is higher under network neutrality. However, allowing for charging over CPs access is welfare enhancing in the short run (Cheng et al. 2011; Choi and Kim 2010). Economides and Hermalin (2012) propose that traffic prioritizing may result in re-congestion because CPs that are prioritized may generate more traffic than under network neutrality, which re-congests the network (Hermalin and Katz 2007). Krämer and Wiewiorra (2012) consider a two-sided market model in which CPs and IUs are congestion-sensitive. They find that charging CPs for prioritized delivery could be more efficient in the short run and results in a higher investment incentive for the ISP. However, the long-run welfare implication depends on the distribution of congestion sensitivity of the CPs. Reggiani and Valletti (2016) further consider the difference between big CPs (that offer multiple services) and small CPs and find that CP tiering leads to a better allocation of network resources. A related stream of literature looks at traffic subsidization in telecommunication. Economides and Hermalin (2015) study the effect of CPs subsidies on their users in the context of online traffic congestion. Ma (2014) examines competition among CPs with direct data usage subsidization. In the model, CPs directly subsidize users Internet connection cost without differentiating the traffic. They analyze how the subsidy affects the choice of data plan. Hande (2009) analyzes a price allocation plan. In the model, the price per unit of data charged by the ISP for connection is split between the CPs and the end users. Closely related to the current study, Cho et al. (2016) analyze a data-sponsoring plan that does not discriminate regarding connection speed. They model the CPs participation in sponsored-data allocation by deciding on the amount of sponsored data. We follow the setting of Cho et al. (2016) and consider a CP s decision to sponsor user traffic as a strategic instrument for differentiation. However, different from their model, we allow heterogeneity in mobile users traffic demands, which allows us to derive distinctive theoretical insights and policy implications. In the next section, we introduce our model and derive equilibrium pricing under different data sponsorship arrangements. We then discuss the consumer surplus implications. Thirty Eighth International Conference on Information Systems, Seoul

5 The Model Model Setup To understand the effects of data-sponsoring programs on consumer welfare and firms pricing decisions, we analyze a model in which a single mobile ISP (the ISP) routes data access between its customers (Internet users, IUs) and two competing service providers (CPs). We assume that all market participants are risk neutral. There is a unit mass of IUs. Users requests for digital content (measured as data packets) follow a Poisson process. To model (vertical) heterogeneity in consumer demand, we assume two types of users, a fraction of heavy users (H) and a fraction 1 of light (L) users. The rate at which the two types of consumers request data packets is given by and, respectively, with >. The two CPs offer differentiated products to the IUs. We model the IUs preference for the two CPs with the classic Hotelling model (Hotelling 1929). That is, we assume that the two competing CPs, indexed by and, are located at locations 0 and 1 on a line of length 1, respectively. The continuum of users of measure 1 are uniformly distributed along the line. IUs incur a fit cost that is proportional (with a unit fit cost of ) to the distance between their ideal content and either of the two CPs (denoted by and 1, respectively). Their locations on the Hotelling line are independent of their request rates ( where {,}), and each consumer chooses only one of the two CPs (the services are mutually exclusive). We denote the gross value of consuming CP s content for heavy and light users as and, respectively. The CPs provide free services to the IUs and profit from the data traffic it acquires. Meanwhile, the ISP adopts a nonlinear pricing scheme: 3 a fixed charge F covers all data usage that is less than a data cap, ; once the data cap is reached, additional data is charged at a rate of. We assume that the data cap is exogenously set at =. In other words, light users only pay the fixed price, while heavy users pay additional charges for traffic consumption beyond. The total payment of a heavy consumer is thus +( ). Formally, the utility that a consumer derives from retrieving content from either of the two CPs is represented as, ()= ( ), and, ()= (1 ) ( ), where {,} and x represents the consumer s location on the Hotelling line. We further assume that > >. Throughout the paper, we assume that the market is fully covered. 4 Similar model setups are used to analyze the economic impacts of network neutrality in the literature (e.g., Choi et al. 2015; Cho et al. 2016; Guo et al. 2013; Kourandi et al. 2015). Following the literature (Cheng et al. 2011; Choi and Kim 2010), we assume that both CPs earn revenue from advertising. The advertising revenue is proportional to the total traffic generated by a CP s users. Let and denote the revenue rates of CPs and, respectively. Without loss of generality, we assume > >0. That is, CP j is more capable of generating profit from consumer traffic. The expected profit of CP () that takes a market share of of either consumer segment is depicted by π = (Cheng et al. 2011). Technically, to avoid trivial solutions, we also assume that the difference in the advertising revenue rate is not too large such that the two CPs are comparable: <. 3 The prevailing pricing paradigm in the broadband market has a nonlinear structure. However, the literature on the economics of net neutrality primarily adopts a linear pricing structure. Pricing with data usage caps is under-investigated, largely due to analytical tractability (e.g., Cheng et al. 2011; Choi et al. 2015; Choi and Kim 2010; Economides and Tåg 2012). 4 Note that > > is a necessary condition for full market coverage. Thirty Eighth International Conference on Information Systems, Seoul

6 Data Sponsorship The ISP can offer a data-sponsoring program and allow the CPs to pay for part of the traffic IUs incurred in using their services. Under the sponsorship, a CP (j) chooses an amount such that consumer traffic up to this amount (and over the data cap covered by the fixed fee, ) will be charged to the CP. We assume that the CP pays the same price as the IUs do to the ISP and that the amount of sponsored traffic satisfies 0,. Expression ISP CP U 0,1 Definition (Mobile) Internet Service Provider Fixed fee for Internet service Data cap under the fixed fee Data fee for traffic exceeding Table 1. Notations Content Providers: there are two content providers and, each located at one end of the interval 0,1 Marginal revenue from a unit of data traffic Amount of sponsored data by CP Users, there are two types of users {,} Data demand of a type-u consumer The gross value of retrieving content for a type-u consumer Disutility resulting from a mismatch Consumer taste, the utility that consumer x of type u gets from using CP j (located at 0) is Table 1. Notations The timing of the game is as follows: in stage 1, the ISP decides whether to introduce a data sponsorship program and how to allocate the option to the two CPs, and announces pricing schemes for users. In stage 2, depending on how the option is designated, either neither, one, or both content providers decide on the number of packets to subsidize their users to maximize their profits. In stage 3, both types of users choose to visit either CP or CP. Model Analysis Baseline Model No Data Sponsorship We analyze the model using backward induction to deduce the sub-game perfect Nash equilibrium of the players actions in each stage. Without sponsorship, if a user of type 0,1 consumes the content of CP, his utility is represented as, ()= ( ), and if the same user consumes the content of CP, his utility is formulated as, ()= (1 ) ( ), where {,}. Under the full market coverage assumption, no users from either segment are excluded from the market in equilibrium. Characterized by their locations and on the Hotelling line, the Thirty Eighth International Conference on Information Systems, Seoul

7 marginal heavy and light users who are indifferent between the two CPs are determined by ( )= (1 ) ( ) and = (1 ), respectively. Because there is no vertical differentiation between the two CPs, we have =1/2 and =1/2. The ISP s pricing decision is then formulated as max, (+( ))+(1 ), such that ( ) 0 and (1 ) 0. The corresponding Lagrange problem is formulated as (+( ))+(1 )+λ 1 ( )+λ 2, where the Lagrange multipliers λ,λ 0 satisfy λ ( )=0 and λ =0. The first order condition w.r.t. to the fixed fee is 1 λ λ =0, while the first order derivative w.r.t. to the variable fee is =λ. Solving the two equations gives λ =>0 and λ =1 >0, suggesting that both constraints are binding at the equilibrium. As a result, we have = and =. Under this optimal pricing strategy, the ISP extracts all surplus of the marginal users in both segments. Its profit is + (1 ). Only One CP Sponsors Data Traffic Let us consider the case in which the ISP introduces a data-sponsoring program, and only CP is allowed to sponsor consumer data traffic. 5 This option enables CP to subsidize a certain amount of data, denoted by 0,, for data traffic that exceeds the original cap incurred by its users. As a consequence, for heavy users who visit CP j, the effective data cap for being charged the fixed fee only is increased to +. The introduction of data sponsorship has no effect on the light user segment, as light users data usage does not exceed the data cap. For a user of type 0,1 who visits CPs and, the utilities can be formulated as, ()= if = if =, and, ()= (1 ) ( ), respectively. The allocation of light users to the two CPs stays the same as before; that is, the light segment is equally split between CP and CP in the equilibrium. Meanwhile, the marginal heavy users who are indifferent between the two CPs are now determined by = (1 ) ( ). We have = +. The data sponsorship helps CP to expand its market share in the heavy segment. A fraction of heavy users who prefer CP will inherently switch to the less attractive CP and incur a higher misfit cost. The size of expansion depends on both the amount of sponsored data chosen by CP and the variable fee set by the ISP. Anticipating this market expansion effect, CP chooses the sponsoring amount to maximize its profit: = + (1 ), 5 The case where CP k is allowed to sponsor data follows a similar logic of analysis; we do not repeat the discussion here given the limited space. Thirty Eighth International Conference on Information Systems, Seoul

8 where = +. Taking the first order derivative of w.r.t. gives + +. For a nonzero variable fee, solving the first order condition yields =. As charging for traffic beyond the data cap curtails CP j s benefit from market expansion, the optimal sponsoring amount is decreasing with the variable fee, (multiplicatively). As a result, the portion of heavy users CP serves is = + = +, the size of which is invariant to the pricing decision of the ISP. This implies that CP j s optimal response to the ISP s pricing decision is to adjust the amount of sponsored data to maintain a fixed share of the heavy segment. To do so, CP j transfers a constant amount of its per-user revenue (i.e., = ) to reimburse the higher fit cost incurred when the marginal heavy users are engaged to visit CP j instead of CP k. It should also be noted that the subsidy is greater than 0 only when >. When the revenue generation rate ( ) is below the threshold ( ), CP will ignore the sponsoring option, and the market equilibrium will resemble the one without the data-sponsoring program. Anticipating CP j s strategic response, the ISP s pricing decision is then formulated as such that max, (+( ))+(1 ), + The corresponding Lagrange problem is formulated as (+( ))+(1 )+λ 1 where the Lagrange multipliers λ,λ 0 satisfy =0. 0 and λ 2, + =0 and The first order condition w.r.t. to the fixed fee is 1 λ 1 λ 2 =0, while the first order condition w.r.t. to the variable fee is =λ 1. Therefore, both Lagrange multipliers are strictly positive at optimality. Essentially, and are determined by the following two equations: + =0, and =0. Solving them simultaneously gives = and =. Again, the ISP extracts all of the marginal users surplus in both the heavy segment (locating at = + ) and the light segment (locating at =). The variable fee increases by compared with the baseline. Interestingly, we find that the introduction of data sponsorship has no effect on the fixed fee. In other words, the two user segments are separable with the ISP s nonlinear pricing scheme. The ISP s profit is given by + +(1 ). The location of the marginal heavy users (i.e., = + ) is increasing in (i.e., CP s ability to monetize the attention it attracts). Note that when is sufficiently large, that is when >3, CP will use the data-sponsoring program to drive CP i out and serve all heavy users. The ISP s pricing decision is then formulated as Thirty Eighth International Conference on Information Systems, Seoul

9 max, (+( ))+(1 ), such that 0 and 0. The corresponding Lagrange problem is formulated as (+( ))+(1 )+λ ( ( ))+λ, where the Lagrange multipliers λ,λ 0 satisfy λ ( ( ))=0 and λ =0. The first order condition w.r.t. to the fixed fee is 1 λ λ =0, while the first order condition w.r.t. to the variable fee is =λ. Again, both Lagrange multipliers are strictly positive at optimality. Essentially, and are determined by Accordingly, we have = (1 ). Both CPs Sponsor Data Traffic ( )=0, and =0. and =. Correspondingly, the ISP s profit is given by + Let us consider a competitive setting in which both CPs are allowed to pay for some of the data traffic that users incur by consuming their content. We denote the sponsoring amounts of CP and CP with 0, and 0,. The utilities for a consumer located at 0,1 from visiting CP and CP can be formulated as, ()= if = if =, and, ()= (1 ) ( ) if = (1 ) if =. As light users do not incur over-the-cap traffic, they are equally split between CP and CP in equilibrium, while the market allocation for the heavy segment, characterized by CP s market share, is determined by = (1 ) ( ), which gives = +. This indicates that CP serves a larger portion of the heavy segment when it is more capable of monetizing attention than its competitor. The size of CP s market share is increasing with the variable fee set by the ISP, which means that data-sponsoring is more effective when the variable cost of traffic is higher. Given the Internet access fee set by the ISP, CPs and choose the sponsoring amounts and, respectively, to maximize their own profits: where = +. = + (1 ), and =( )(1 )+ (1 ), The first order condition + + =0, and + +( ) =0. Thirty Eighth International Conference on Information Systems, Seoul

10 For a nonzero variable fee, solving the two equations simultaneously yields = and =. It should be noted that both subsidies are meaningful only when 2 + >3. Otherwise, the weaker CP (i.e., CP ) will ignore the sponsoring option, and the market equilibrium will consequently degenerate to that where only CP is allowed to provide data sponsoring. The portions of heavy users that the two CPs serve are = + = + and (1 )= + = +, respectively, both greater than 0 with <. Interestingly, when both CPs can sponsor data traffic, their market shares are independent of the Internet access fees charged by the ISP. In other words, irrespective of the pricing decision of the ISP, the two CPs best responses with respect to the amount of sponsored data will always lead to a static allocation of heavy users. In such an equilibrium, a fraction of heavy users who prefer CP will inherently switch to CP j and incur higher fit costs. Note that if there were no competition, CP could transfer to compensate for the loss in utility that a marginal heavy user could derive from visiting the alternative CP. However, competition from CP mandates a larger amount of compensation (because = >0 when 2 + >3). Subsidies from both CPs are decreasing with the fit cost. In other words, competition in the sponsoring program is less intense when the fit cost is higher. Anticipating the two CPs strategic responses, the ISP s profit maximization problem is formulated as such that The corresponding Lagrange problem is (+( ))+(1 )+λ max, (+( ))+(1 ) + 0 and λ, where the Lagrange multipliers λ,λ 0 satisfy λ + =0 and λ =0. The first order condition w.r.t. to the fixed fee is 1 λ λ =0, while the first order condition w.r.t. to the variable fee is =λ. Essentially, and are determined by the following two equations: We have = and =. + =0, and =0. With both CPs allowed to offer sponsored data services, the ISP again extracts all of the surplus of the marginal users by properly setting the fixed and variable cost of data traffic. The variable fee is even higher than in the case where only one CP is allowed to sponsor data, while the fixed fee stays the same, as we would expect. The ISP s profit is + +(1 ). Thirty Eighth International Conference on Information Systems, Seoul

11 Impacts of Sponsored Data Services The ISP s Profit Based on the equilibrium results derived in the last section, we compare the ISP s profits under different sponsoring scenarios. 6 With no data sponsorship, the ISP s profit is +(1 ). When only CP is allowed to provide data sponsorship, the ISP s profit is given by + +(1 ) when CP serves only part of the heavy segment, or +(1 ) when CP serves the full heavy segment. When, CP has no incentive to sponsor a positive number of packets even if it is allowed to do so, and consequently, the market equilibrium coincides with that without data sponsorship. When >, comparison of the above expressions reveals that the ISP earns a strictly higher profit than it does without data sponsorship. The increase is partially attributable to the fact that the sponsored data program provides a device for the ISP to extract the CP s profit, which is otherwise out of its reach. A similar result can be expected for the case where only CP is allowed to sponsor data traffic incurred by using its services. Therefore, we have the following proposition. Proposition 1. Compared with the baseline scenario without data sponsorship, the ISP is never worse off if it allows one of the CPs to sponsor users data usage. When both CPs are allowed to offer data sponsorship, the ISP s profit is + +(1 ). It has been noted that when > and 2 + 3, only CP will provide a nonzero subsidy to heavy users. Under this market condition, the market equilibrium is the same as that when only CP is allowed to sponsor user data traffic. When 2 + >3, comparing the ISP s profits in the last two scenarios shows that offering the data sponsoring option to both CPs leads to a higher profit for the ISP, because the competition for the market share in the heavy segment drives both CPs to transfer more of their per-user revenues to the ISP. This discussion leads to the following proposition. Proposition 2. The ISP s optimal strategy, regardless of market conditions, is to allow both CPs to sponsor users data usage at their own discretion. The reason is that the ISP can extract the CPs profits and the consumer surplus when either CP finds the provision of data sponsorship profitable. As a summary for the discussion regarding ISP profit, we illustrate the effective market equilibria under different market conditions in Figure 1. Note that the marginal lines are determined by. We make a few observations based on this ratio. First, as increases, the no sponsoring region becomes larger. In practical terms, a large fit cost per unit of distance can be interpreted as a market condition in which the horizontal differentiation is high. When the fit cost is large, a CP will find it increasingly difficult to attract users by increasing the amount that it sponsors. Therefore, CPs become more likely to forgo the benefit brought by the data sponsoring option in market expansion. Second, an increase in will also shrink the region in which both CPs choose to sponsor. When both CPs engage in the sponsoring competition, as increases, the weaker CP will start to find data sponsoring less affordable and will revert to no sponsoring, thus retreating first from the competition. It can never happen that the weaker CP 6 Due to space limit, we only present a brief discussion of the profit and welfare comparison. A full analysis is available upon request. Thirty Eighth International Conference on Information Systems, Seoul

12 prefers to sponsor while the superior one does not. From Figure 1, we can see that if >, the case in which only CP k is allowed to sponsor is not an equilibrium result. In other words, allowing only the CP with a lower revenue rate to sponsor is never optimal for the ISP. A = C =, B = D =. Figure 1. Market Equilibria with a Data Sponsoring Program Consumer Welfare Comparison We now compare consumer welfare across the three scenarios. Denote the marginal heavy and light users in different market equilbiria uniformly by and. Regardless of the data sponsorship settings, the two indifferent users realize the lowest net utilities, and the ISP sets the Internet access fees and to extract all of their consumer surplus. We use the indicator functions 1 and 1 to track whether CP and CP, respectively, choose to subsidize users usage in equilibrium. The net utilities of the marginal users can then be summarized as follows: 1 =0, and (1 ) 1 =0. If a consumer located at decides to consume content from CP, his utility can be represented by Otherwise, it is given by In all of the above formulae, {,}. 1 =( ). 1 (1 )=( ). As a consequence, the expression that depicts consumer surplus is given by Thirty Eighth International Conference on Information Systems, Seoul

13 CS= ( )d+ +(1 ) ( )d+ ( )d ( )d = + +(1 ) +. This reveals that the consumer surplus is determined by the fit costs incurred by the two marginal users. The intuition is that no matter how much either CP subsidizes its users, the effective parts of the subsidies are only commensurate with the compensation needed to cover the fit costs of the most distant users, while the rest are eventually extracted by the ISP with its nonlinear pricing scheme. Replacing with results in a function dependent soley on : + +(1 ) + = ( 1)+ (1+). Different from, varies across different scenarios; that is, in the case of no data sponsorship, = ; in the case where CP j is allowed to sponsor consumer data traffic only, = + case where both CPs are allowed to sponsor, = +. or 1; and in the Because the first term in the last expression is increasing in (i.e., CP j s market share in the heavy segment) for, the overall compensation increases as the market partition point shifts from to 1. This leads to the following proposition. Proposition 3. Consumer surplus is uniformly improved as long as some CP is allowed to sponsor users data usage. When 2 + >3, both CPs provide effective data sponsorship. Because + <0, the provision of a nonzero subsidy from CP k shrinks CP j s market share of heavy users. We have the following proposition. Proposition 4. Compared with the scenario where the weaker CP is deprived of the data sponsorship option, the competition between the two CPs makes users worse off. In our model, a sponsored data plan does not necessarily benefit consumers because the monopolist ISP makes the final pricing decision to extract the consumer surplus, which makes the utility of the marginal consumer zero (full market coverage assumption). An important observation about the value of the consumer surplus is that the ISP can more easily extract the consumer surplus when the marginal consumer =1/2. In other words, the consumer surplus is higher when the location of the marginal consumer is far from ½. When there is competition between two CPs, the location of the marginal consumer can be closer to ½ because both CPs are allowed to sponsor consumers data traffic. Therefore, the consumer surplus can be smaller when both CPs sponsor. Discussion In the main model, we assume that =. Actually, we can show that in the case of no data sponsoring, the cap does not matter. That is, can be larger or smaller than. The ISP always obtains the same optimal profit. If λ <λ, the ISP s profit maximization problem is formulated as follows: max, (+( ))+(1 )(+( )), such that, ( ) 0 and, ( ) 0. Essentially, and are determined by the following two equations: = Thirty Eighth International Conference on Information Systems, Seoul

14 , ()= ( )=0,, ()= ( )=0. Therefore, = and = ( ). Then, the ISP s profit is +(1 ) /2, which is the same as the ISP s profit when =. If λ >λ, the ISP s profit maximization problem is formulated as follows: max, (+( ))+(1 ), such that, ( ) 0 and, ( ) 0. Essentially, and are determined by the following two equations: Therefore, = and =., ()= ( )=0,, ()= =0. The ISP s profit is still +(1 ) /2, which is the same as the ISP s profit when =. Therefore, in the case of no data sponsoring, we can assume that = without loss of generality. Conclusion In this study, we examine the economics of sponsored data services with a formal model analysis. We consider the interaction among three types of players in a broadband market (a monopolist ISP, two CPs, and a continuum of IUs), and derive the equlibrium strategies that the ISP chooses to maximize its profit under different data sponsorship settings. Our modelling approach provides a comprehensive framework to understand the incentives of the players associated with sponsored data services: for IUs, data sponsorship reimburses excessive data usage; for CPs, it provides a way to get a leg up on the competition in the content market; for the ISP, it is a device to extract the profits of CPs that are otherwise out of its reach. We also take into account heterogeneous data usage patterns among IUs together with their horizontally differentiated tastes for digital content. The analysis of data sponsorship in such contexts gives us a better understanding of the issues surrounding an ISP s price discrimination and CPs provision of digital content. We find that the ISP s optimal strategy, irrespective of market conditions, is to make this option available to both CPs and allow them to choose whether to do so at their own discretion. When either CP finds the provision of data sponsorship profitable, the ISP can extract the CP s profit as well as more consumer surplus from some segments of the market. However, such a market equilibrium outcome would make IUs worse off compared with the scenario in which the data sponsorship option can only be offered by the CP that can make more profit from consumer data traffic. Policy makers may consider adding constraints on the eligilbity to sponsor data traffic. Our research is not without limitations. In our model, the data cap is exognenously determined. While we can show that the amount of the cap does not influence equilibrium behavior, it would be interesting to examine models with an endogenous data cap. Meanwhile, we consider a non-linear pricing menu, whereas it might be interesting to explore whether our insights hold when the ISP adopts other forms of pricing schema (e.g., a linear pricing strategy). Acknowledgements The authors would like to thank the support from the National Natural Science Foundation of China [Grant ] and the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU ]. Thirty Eighth International Conference on Information Systems, Seoul

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