Perpetual vs. Subscription Licensing of Software with Network Externalities1 Jie Jennifer Zhang College of Business Administration, University of Texas at Arlington, Arlington, TX 76019
[email protected] Abraham Seidmann William E. Simon School of Business, University of Rochester, Rochester, NY 14627
[email protected] The “Software-as-a-Service” (SaaS) model, which delivers software over the Internet on a subscription basis, has been growing rapidly. Software vendors use this leasing model to replace or supplement the traditional perpetual licensing (selling) model. This paper looks at a software vendor who can sell (at a posted price) or lease his product, where he guarantees that the subscribers will always have the latest version of the software. We discuss the optimal way to license software: the selling model, the leasing model, or a hybrid approach that involves both. We then address some of the specific issues in the packaged software market, including network externalities, upgrade compatibility, and commitment on pricing in a dynamic environment. We demonstrate how a software vendor can manage the trade-offs of selling and leasing to achieve a higher profit, as well as the corresponding welfare effect on consumers. Our paper also contributes to durable goods theory by showing that network externalities have a significant effect on the vendor’s licensing decisions.
1. Introduction Software traditionally has been sold as a property. Users pay a fee for a perpetual proprietary license to use it. Advances in information technology, especially the Internet, have unleashed unprecedented levels of process innovation as well as product innovation in the software industry. As a result, software publishers can deliver the software product together with maintenances and upgrades to the users over the Internet—the so-called Software-as-a-Service (SaaS) model. For example, starting at $65/user/month, SalesForce.com provides OnDemand Customer Relationship Management software solutions to its users over the Internet. Proponents of SaaS claimed that it can lower the cost of ownership and grant users access to the up-to-date software at a predictable cost without the large upfront investment. Therefore, they claimed that “traditional (way of delivering) software is already dead” (The Economist 2006). The Economist (2006) estimates that the market for SaaS is growing at 50% each year. Following this rapid growth of SaaS, many large Independent Software Vendors (ISVs) have adjusted their licensing policies:
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The authors gratefully acknowledge the support of the .NET Institute in this research project. We also apprediate the careful review of
the anonymous reviewers at CIST 2007. Due to page limit, proofs and some figures were removed.
Microsoft started offering the Enterprise Subscription Agreement to business users at an annual rate on a threeyear term; other software vendors like Sun, Oracle, SAS Institute, Computer Associates and BMC also offered their own subscription licenses on their major products. Despite the various forms of business models, those vendors all aim at generating perpetual revenue streams by transforming such a durable good as software into subscription-based services—a form of “leasing”. International Data Corp. (IDC) recently announced “the SaaS model will help drive a transition to subscription licensing” as one of its top ten predictions for the software industry (TenWolde and Konary 2006). With those encouraging speculations, software vendors still need to think carefully before they move completely to a subscription model. In order to provide the strategic suggestions to software vendors as well as recommendation to users, we will address the following questions in this paper: (1) What are the benefits and costs of the subscription licensing to the vendors? (2) Trading off those benefits and costs, what is the optimal way for the vendor to license software? Should they use subscription licensing to completely replace the traditional perpetual licensing? (3) How are users influenced by this trend of subscription licensing? By addressing the specific issues related to implementing the licensing policies, such as compatibility, network externalities and commitment, our paper fills a gap in the literature by examining the impact of network externalities on a monopoly seller’s licensing strategies. We investigate how a monopolist software vendor can use different licensing policies and pricing schemes to segment consumers based on their sensitivity to product quality and realize second-degree price discrimination. Considering consumers’ self-selection behavior, we identify the vendor’s optimal licensing policy — selling or leasing exclusively, or adopting a hybrid strategy of both selling and leasing. In this way, our paper extends the previous studies of Padmanabhan et al. (1997), Fudenberg and Tirole (1998), and Ellison and Fudenberg (2000), which focus on product upgrade strategies in selling durable goods without considering leasing. Our results challenge the claims of Coase (1972), Swan (1977), Bulow (1982), and others that leasing can be used to resolve the time-inconsistency problem in a market for durable goods. Bulow (1982), for example, concludes that a lessor “can achieve all the standard results of a nondurable monopolist.” Using a similar setting, we find that this claim does not hold once the network effect is large enough.
2. Literature Review Many economics and marketing researchers have examined the ways of selling durable goods. Coase (1972) raised the conjecture that a monopolist seller of such goods could not sell them at the monopoly price because 2
rational and patient consumers would anticipate a future price drop and delay their purchase. Coase also argued that leasing durable goods would solve this time-inconsistency problem. Bulow (1982) formally proved the Coase Conjecture and further affirmed that leasing can achieve the optimal profit for a monopolist seller of nondurable goods; other studies verifying the conjecture followed, including, for example, Stokey (1981) and Gul et al. (1986). We adopt a setting similar to the traditional Coase Conjecture, where a monopolist software vendor sells or leases his product to the market. We want to examine the impact of the network externality effect alone on the vendor’s strategy; we exclude from consideration the potential impact of competition and product depreciation. Among the research on distribution strategies for packaged software, Choudhary et al. (1998) study the problem of renting software from a different point of view, arguing that renting software in the first period to those who otherwise would adopt later can increase the seller's profit. Others focus on the optimal pricing under a pay-per-use model in an on-demand computing environment (Huang and Sundararajan, 2005; Ma and Seidmann, 2004). Our work also draws on the literature on markets that involve either direct or indirect network effects. Katz and Shapiro (1994) categorize such markets and identify the issues firms and consumers face in them. Farrell and Saloner (1986) investigate how the installed base of products with network externalities interacts with a firm’s incentive to innovate, and they evaluate the welfare implications of various strategies that firms might adopt. Katz and Shapiro (1985), Farrell and Saloner (1985), and Choi (1994), consider similar issues. Brynjolfsson and Kemerer (1996) construct a hedonic model and empirically report that the network externalities significantly contribute to the successfulness of spreadsheet products: a one percent increase in a product's installed base was associated with a 0.75% increase in its price. Network externalities and compatibility problems significantly complicate the software vendor’s strategic decisions. The most current attempt in this area is Ellison and Fudenberg (2000) that examine software upgrades with network externalities. They find that the network effect causes excessive upgrades beyond the socially optimal level. Our paper builds models with a similar setting as Ellison and Fudenberg (2000), but looks at the seller’s licensing and pricing strategies. Building on the above literature, we investigate whether leasing can effectively solve the timeinconsistency problem for special durable goods with network externalities like software — that is, whether web-based subscription will become the dominant way of licensing software. Moreover, we discuss leasing and selling software in a monopolist context and explain the motivation for different strategies. Finally, we analyze 3
the optimal software distribution strategies and discuss their impact on users to provide vendors with insights on optimal licensing policies and increase understanding of the effect of such policies on both consumer and social welfare.
3. Models We examine intertemporal consumer behaviors and the firm's strategic licensing policy with a two-period model. We introduce our model by first detailing the assumptions about the players—consumers and a monopoly software vendor. Assume consumers of the software product have the following form of net utility: U (q, x, p; θ , e) = θ q + e x – p,
(1)
where p and q are the price paid and the quality of the software product, respectively; x represents the mass of the adopters of the product; and θ and e are the intensity of the quality preference and the network externality effect, respectively. Software “quality” includes such dimensions as speed, compatibility with available operating systems, functionality, user interface, ease of learning, and other characteristics that affect the users’ valuation of the product. Consumers are heterogeneous in their quality preference θ but are homogeneous in their sensitivity to the network externality effect e. Consumers are indexed by θ and are uniformly distributed on the support [0, 1]. Assume that there are two versions of the software. Version I, with quality q1, is released at the beginning of period 1. Because of ongoing development, an upgraded version with quality q2 is released at the beginning of period 2, and q2 > q1. A stochastic q2 will not change our major results and insights, hence, in order to simplify the problem and to emphasize the main results, we here only present cases in which q2 is fixed.2 The software vendor can control the quality improvements by controlling R&D investments. The vendor therefore is supposed to make decisions in three stages: in stage 1, he decides which licensing policy to follow: pure selling, pure subscription or a hybrid policy; in stage 2, he decides the optimal quality level of version II, trading off investments and returns; in stage 3, he takes the licensing strategy and q2 as given and decides the optimal pricing to maximize his total profit over the two periods. Since the cost of quality improvement is independent of the licensing strategies, in order to simplify the model and focus on licensing policy only, we can omit the quality selection decision (stage 2) by assuming q2 is exogenous.3
2
Zhang and Seidmann (2002) considered the same problem under the case where q2 is stochastic. Choudhary (2007) solves the quality selection problem in a pure selling and a pure subscription market without the network externality effect. 3
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Consumers discount their future utility gain by a factor of β ∈ [0,1] . To reduce the number of cases under consideration, we assume that q1 ≥ β ⋅ (q2 − q1 ) , so that consumers with a higher quality preference θ
prefer adopting version I of the software in period 1 as opposed to waiting to buy version II. Similar to Assumption 1 in Fudenberg and Tirole (1998), this assumption only excludes very large improvements between the two versions, but it considerably simplifies the analysis. We study the most common case of software production in terms of compatibility: the software is backward compatible but forward incompatible. That is, the later version of the software can successfully use interfaces and data formats from the earlier, but the original software is not designed in such a way that it can seamlessly accommodate files produced with the planned future version. Hence, consumers who upgrade or buy later can enjoy network externalities from the users of both versions, but those who continue using the older version have network externalities only from users of the same version. The software vendor can provide the software to the market through one-time sales, subscriptions, and sales of upgrades. Suppose that the marginal production cost of software is zero. The software vendor sets the selling price for version I of the software, p1, the upgrade price, pu, and the second-period selling price for version II, p2, if he uses the selling policy. If the software vendor adopts a subscription policy, he decides on the per period rent, pr, at the beginning of period 1, and he commits to keeping this rent the same during the two periods. Since the software vendor cannot tell whether the demand for version II software is from one who already owns the first version or a new adopter, the incentive compatibility condition requires the vendor to keep the upgrade price pu no higher than the selling price of version II; that is, pu ≤ p2. Otherwise, everyone will choose to buy the new version rather than upgrade. Period 1 SV provides Version I
Period 2 SV provides Version II
Upgrade to version II Buy version I Retain version I Auto upgrade to version II
Lease version I
Buy version II Inactive Inactive Figure 1: Consumer Strategies.
For a consumer of type θ who purchases version I of the software in the first period, the total discounted value can be expressed as 5
VB (θ ) = U (q1 , x0 , p1 ;θ , e) + β max{U (q2 , x2 , pu ;θ , e),U (q1 , x1 ,0;θ , e)} .
(2)
In the second period, the buyer can decide to upgrade the software following the buy-and-upgrade (BU) strategy or adopt the buy-and-hold (BH) strategy and keep using version I. Here x0 denotes the mass of adopters of the software in the first period (including both buyers and subscribers), x1 denotes the mass of consumers who continue using version I in period 2 (those buyers in period 1 who do not upgrade to the new version), and, by our backward-compatibility assumption, x2 is the mass of total adopters of either version in period 2 (including all the buyers in either period and the subscribers). After entering a lease contract, the subscriber receives a continuous supply of software services with updates for a fixed per period payment. The value is VL (θ ) = U (q1 , x0 , pr ;θ , e) + β U ( q2 , x2 , pr ;θ , e) .
(3)
Thus a subscriber is obliged to pay the rent pr every period based on the lease contract. Finally, the expected discounted value for a consumer inactive in the first period is VI (θ ) = β max{U (q2 , x2 , p2 ;θ , e),0} = β max{θ q2 − p2 ,0} .
(4)
If a consumer is inactive in the first period and waits to buy the second version software in period 2 if her net utility from such a purchase is nonnegative, we call this strategy as IB; and we call the strategy of remaining inactive for both periods as II. Given the vendor’s licensing policy, a consumer of type θ will choose to buy or lease the software or be inactive in the market by maximizing the expected total discounted value over the two periods: V (θ ) = max{VB (θ ),VL (θ ),VI (θ )} .
(5)
The software vendor will make licensing policy and pricing decisions taking into account consumers’ adoption strategies. In order to show the impact of the network externality effect on the vendor’s choice of licensing policy, as well as on the consumer surplus, we compare the strategies for the software vendor under various combinations of market conditions—with and without a network externality effect and when the software vendor can and cannot commit to future prices. Specifically, we solve the market equilibria under the pure selling, pure subscription, and hybrid strategies for each of the four cases presented in Sub-sections 3.1 to 3.4.
3.1. Case 1—No externalities, with commitment We use this case as our benchmark case, which considers the case in which there is no network externality effect (e = 0), and the seller can commit to second-period prices pu and p2 in advance. 6
3.1.1.
Pure subscription
Suppose that the software vendor offers a take-it-or-leave-it subscription contract over two periods. If a user takes it, she will pay rent pr at the beginning of each period and enjoy the latest version of the software without any additional charge. If the software vendor only offers subscriptions but does not sell the product, a consumer has only two choices: to subscribe or not to subscribe. From Equation (3), a subscriber’s total value over the two periods is VL (θ ) = θ (q1 + β q2 ) − (1 + β ) pr , and the value for someone who does not subscribe is VI (θ ) = 0 . Thus consumers are segmented into two groups: those with θ ≥
pr will subscribe; ( q1 + β q2 ) /(1 + β )
others will stay inactive. Solving the maximization problem gives the optimal subscribe price pr = The software vendor receives profit Π L = 3.1.2.
q1 + β q2 . 2(1 + β )
q1 + β q2 . 4
Pure selling
This is the scenario considered in Fudenberg and Tirole (1998). If the software vendor only provides perpetual licensing of the software, consumers’ decisions include whether to buy in period 1 or in period 2, and, if they buy in period 1, whether to upgrade in period 2. At the beginning of period 1, a consumer of type θ evaluates her value of either of the two choices: V (θ ) = max{VB (θ ),VI (θ )} . There exists a trade-off between buying and waiting: if the consumer buys the software in period 1, she can use it in period 1 and has the option to upgrade in period 2; otherwise, she cannot use the software in period 1, but she does retain the option of buying the new version in period 2. Based on Equations (2) and (4), a user has four choices over the two periods: buy version I in period 1 and upgrade to version II in period 2 (BU), buy version I in period 1 and keep using it in period 2 (BH), wait to buy version II in period 2 (IB), or remain inactive in both periods (II). Since q1 ≥ β ⋅ (q2 − q1 ) by the assumption we made above, we have the potential market segmentation as described in Lemma 1 and shown in Figure 2. Lemma 1: Consumers with quality preference θ ∈ [0,θ 0 ] are inactive; those with θ ∈ (θ 0 ,θ1 ] wait to buy version
II in the second period; those with θ ∈ (θ1 ,θ 2 ] buy version I in period 1 but do not upgrade in period 2; and those with θ ∈ (θ 2 ,1] always use the latest version of the software during the two periods. II 0
BH
IB
θ0
θ1
BU
θ2 7
1
Figure 2: Potential market segmentation under pure selling.
Taking consumers’ choices into account, the software vendor optimizes his discounted total profit over the two periods. Then, the software vendor’s optimal total profit in Case 1 is the same as under the pure leasing policy. The equilibrium consumer segmentation is given by xBU = xII =
1 . It therefore is optimal for the software 2
vendor to sell only to high-end users and to price out buy-and-hold users as well as the opportunistic consumers who wait to buy in period 2. Although consumers who purchase the software in the first period do have the option of not upgrading in period 2, the option has no value here since the prices are set such that all of them will choose to upgrade. Since the software vendor has no preferred strategy, all the prices, allocations, and profits will be the same as in a hybrid market. Comparing the licensing policies in this scenario, we have Proposition 1: When there is no network externality effect, but the vendor can commit to second-period prices,
in equilibrium, the vendor achieves the same profit under any strategy. The total consumer surplus likewise does not vary among the three licensing policies. Consumers are segmented into two parts, with half of the consumers indifferent with respect to leasing or buying and upgrading and the other half inactive in both periods.
3.2. Case 2—No externalities, no commitment The benchmark case assumes that the seller is able to commit to the second-period prices. To see the effect of this assumption, we look at the software vendor’s decision on whether to deviate from the committed prices. The software vendor’s optimal price of version II and upgrade price are p2* =
q2 q2 (q − q ) < , pu * = 2 1 . The optimal 4 2 2
upgrade price is the same as the “commitment” price, but the optimal selling price in period 2 is lower than the “commitment” one. Thus the optimal decision in the benchmark case is not consistent across time: given the chance to reconsider his decision at the beginning of period 2, the seller would be better off to lower the secondperiod price, which was announced in period 1. The seller’s commitment therefore is not credible, which accords with the Coase Conjecture that rational consumers anticipate that prices will fall. In this section, we consider the Nash Equilibrium without commitment: the seller cannot commit to preannounced second-period prices pu and p2, unless there is a binding contract such as a lease. Since subscription is a way of committing to the second-period price by introducing an external constraint, the market equilibrium under the subscription policy will be the same as in the benchmark case. 8
3.2.1.
Pure selling
Given the price of version I of the software, consumers decide whether to buy. The vendor sets the price of version II and an upgrade price at the beginning of period 2. Consumers then choose whether to upgrade, if they buy the first version, or, if they have waited, whether to buy version II or remain inactive. We solve this two-stage Stackelberg game backward, starting from the decision in the second period, taking the first period’s outcome as given. In the second period, a consumer who has bought the software may or may not upgrade, depending on her type, the price of the upgrade, and the quality improvement. A consumer who did not buy the software in period 1 will buy the new version in the second period when her net utility is nonnegative. The potential market segmentation over the two periods is as depicted in Figure 2 as well as in Equations (9) and (11) in §3.1.2. Thus, a consumer’s strategy in the second period depends on her type and the two cutoff values θ 2 = pu /( q2 − q1 ) and θ 0 = pu / q2 . Given the consumers’ strategies, we study the second-period profitmaximization problem under the constraint that each of the four segments is nonnegative, which yields the optimal upgrade price and full price of version II given p1. Taking the second-period equilibrium into consideration, the seller maximizes his total profit by deciding the first-period price p1 . The constrained optimal solution is p1 =
(1 + β )q1 β q2 (1 − ) , and 2 2(1 + β )q1 − β q2
xBH = 0 . The optimal profit under this pure selling strategy is less than the optimal profit in the benchmark case, which is also the profit under the pure leasing strategy. This profit reduction is caused by the vendor’s inability to make a credible commitment. It seems that when the network effect is ignored, leasing does solve the timeinconsistency problem, as suggested by Coase (1967) and others. Consumers are better off due to the option of upgrading and leapfrogging under this pure selling strategy. The increase in the consumer surplus is even greater than the loss in the software vendor’s profit, so the social welfare is also higher than in the benchmark case. 3.2.2.
Hybrid
Consumers in the top segment have the greatest preference for quality and would like to adopt the latest version of the software. Since both leasing and buying version I and then upgrading enable consumers to use the latest version software, they receive the same value, but at different costs. When the software vendor provides both leasing and buying options, those consumers will make the decision about licensing by comparing the costs.
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Consequently, the market segments of leasing (L) and buy-and-upgrade (BU) will not co-exist unless the costs are the same, under which condition those high-end consumers are indifferent to either strategy. For simplicity and without loss of generality, we only consider consumers’ pure strategy of leasing and buying and ignore their mixed strategy of either buying or leasing. Thus, for the hybrid case to exist, we need to have (1 + β ) pr < p1 + β pu . The potential market segmentation is shown in Figure 3. II 0
BH
IB
θ0
θ1
L
θ2
1
Figure 3. Potential market segmentation under the hybrid strategy. When the vendor cannot commit to second-period prices, he will set a per period rent pr and the price for version I of the software p1 at the beginning of period 1. Consumers then choose to lease, buy, or remain inactive. In period 2, those who lease receive an automatic upgrade; after the vendor sets the price for version II of the software p2 and the upgrade price pu, those who adopted version I decide whether to upgrade, and those who were inactive in period 1 decide whether to buy the new software. Solving the two-stage game backward, we consider the vendor’s second-period profit-maximization problem first. Taking the second-period equilibrium into consideration, the seller maximizes his total profit by deciding the first-period price p1 . Solving the constrained optimization problem, we find that in equilibrium, all adopters in period 1 will upgrade in period 2: xBH = 0 . Proposition 2: In equilibrium, when a software vendor cannot commit to future prices and there is no network
effect, a pure leasing strategy yields the highest profit to the vendor, a pure selling strategy yields the lowest, and a hybrid strategy yields a profit in between: Π S < Π H < Π L . The rankings of the consumer surplus and the social welfare are in the opposite order: CS L < CS H < CS S , W L < W H < W S . Since the profit from leasing and the market segmentation are the same for Cases 1 and 2, Propositions 1 and 2 suggest that being unable to make a commitment does decrease the software vendor’s profit.
3.3. Case 3—with externalities, with commitment Software has a strong network effect because there is value created through file sharing and knowledge exchange among adopters, as well as from compatible products. Users therefore will value a software product with a greater network effect more. In this case, we study the market equilibrium when we add a network
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externality effect to the benchmark case; that is, the seller can commit to second-period prices in advance, and there is a significant network effect in the market. 3.3.1.
Pure subscription
The expected discounted value for a consumer who leases is given in Equation (3). Consumers with
θ ≥ θ L will get positive utility from the contract. The level of θ L can be obtained by solving VL( θ L) = 0. Given the consumers' choices, the vendor sets the optimal rent in the lease contract to maximize his total discounted profit from the two-period contract. Within a reasonable range of the network externality effect, (1 + β )e ≤ q1 + β q2 , the market is not fully covered in the sense that not everyone in the market will lease. The optimal price schedule for the vendor is pr =
q1 + β q2 , which is the same as in the case without network 2(1 + β )
externalities. The mass of active consumers increases with the network externality effect. Comparing with the benchmark case, network externalities increases the vendor’s market share and profit, as well as each user’s utility. Both the vendor and the consumers are better off. 3.3.2.
Pure selling
With the ability to commit to future prices, the software vendor announces the selling prices of version I, p1, and version II, p2, as well as the upgrade price, pu, at the beginning of period 1. Consumers make the decision whether to buy version I at that point; in period 2, those users who bought version I can choose to upgrade to version II or to keep using version I. Consumers have the same four strategies as in the benchmark case: {II, IB, BH, BU}. The potential market segmentation therefore is also the same as that described in Lemma 1. The cutoff values for the four segments are θ 0 , θ1 , θ 2 and θ 3 , as in Figure 2. Taking into account the consumers' self-selection behavior, the software vendor sets prices p1 and pu to maximize his discounted total profit over the two periods. The above game has two possible equilibria: Equilibrium I: When the ratio of the second-period quality to the first-period quality, q2/q1, is relatively high, some medium-value consumers will prefer to wait to buy the software in period 2. This “leapfrog” behavior lowers the vendor’s profit in period 1. It therefore is optimal for the vendor to price his products so that those consumers either buy and upgrade or do not buy the software in either period. The resulting market segmentation is (II, BU). In this equilibrium, the market segmentation, the vendor’s profit and the consumer surplus are identical to those under the pure leasing strategy.
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Equilibrium II: when the ratio q2/q1 is relatively low, the vendor gives up those “leapfroggers” but expands his market share by lowering the first-period selling price. The resulting market segmentation is (II, BH, BU). We denote the equilibrium prices as p1S , puS , and p2S , and the equilibrium profit, consumer surplus, and social welfare as Π S , CSS, and WS, respectively. Contrary to the results in Cases 1 and 2, where the network externality effect was not considered, the vendor will not be better off if he chooses to lease rather than sell. 3.3.3.
Hybrid
With network externalities, the potential market segmentation for the hybrid case is the same as that in the benchmark case in Figure 3. Consumers can choose to lease and receive the upgrade in period 2 (type L), buy in period 1 but not upgrade in period 2 (BH), wait to buy until period 2 (IB), or remain inactive in both periods (II). Those consumers who lease have a utility function VL (θ ) = θ (q1 + β q2 ) + e( x0 + β x2 ) − (1 + β ) pr . The utility functions of the other three types of consumers are the same as in the pure selling case. There are two equilibria under the hybrid policy that lead to the same market segmentations and vendor profits as those for the pure selling strategy: Π H = Π S . Therefore, in this case, adding the subscription licensing policy does not improve the vendor’s profit from pure selling. Because the quality is public knowledge, the total discounted price charged to lease and buy-and-upgrade customers should be the same in order to eliminate an arbitrage opportunity: prH =
p1S + β puS . 1+ β
Proposition 3: In equilibrium, when a software vendor can commit to future prices and there is a network effect,
a pure selling or hybrid policy weakly dominates a pure subscription strategy for the vendor: Π L ≤ Π S = Π H . The consumer surplus and the social welfare are also not higher when the vendor chooses to lease:
CS L ≤ CS S = CS H , W L ≤ W S = W H . Compared with a pure subscription licensing strategy, the vendor has a greater market share and higher profit, and consumers are also better off, under a pure selling or hybrid policy. Medium-value consumers can buy in period 1 without upgrading in period 2 under the pure selling or hybrid policy, but they are forced to enter a lease contract under the pure subscription policy. A purchase option therefore significantly reduces those users’ cost of using the software. Consumers who always use the latest version of the software thus incur a higher total cost. Their loss, however, is outweighed by other consumers’ gains, so the total consumer surplus is greater.
3.4. Case 4—with externalities, no commitment
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In this subsection, we consider the case when the monopoly vendor cannot make a credible commitment to a second-period selling price, while there is a network externality effect in the market. Since leasing is a way to commit to future prices, the vendor’s inability to commit to future selling prices does not affect the subscription strategy. Thus, the equilibrium of this case under the pure subscription policy is the same as that in Case 3. We next solve for the equilibria under the pure selling and hybrid policies and compare them with that under pure subscription licensing. 3.4.1.
Pure selling
If a vendor cannot commit to second-period prices, rational consumers will expect the vendor to reduce the pre-announced upgrade price and price of version 2 in period 2. The vendor therefore will wait to decide the second-period prices until after consumers’ first-period decisions have been made; that is, the vendor determines the second-period prices based on the price and market share realized in period 1. We solve this two-stage Stackelberg game via backward induction. The consumers’ choices are the same as in 3.3.2, and so is the potential market segmentation (II, IB, BH, BU). In period 2, the software vendor takes the consumers’ selections in period 1 as given and maximizes his second-period profit alone by choosing the optimal upgrade price and the price to purchase version II. Then the vendor finds the optimal first-period selling price, taking into consideration consumers’ best response to his second-period pricing solved above. These prices directly affect consumers’ adoption decisions, which determine the vendor’s market share. The market share in turn shapes the network externalities, which also influence consumers’ utility. As discussed in 3.3, when the vendor can commit to future prices, the network effect gives the vendor an incentive to lower the first-period price to increase its market share as well as the utility obtained from future adopters, so that he can charge a higher upgrade price and purchase price in period 2. If the vendor cannot commit to high prices in the second period, some medium and low-value consumers will wait to buy version II after the installed base has been built up and they can benefit from both the network effect and the lower price. In equilibrium, consumers strictly prefer such waiting to a buy-and-hold strategy, resulting in the market segmentation (II, IB, BU). 3.4.2.
Hybrid
If the vendor chooses both to sell and to lease the software product, he can commit to the upgrade price but still leaves the option of “leapfrogging” available. The equilibrium market segmentation is (II, IB, L). Similar to the game presented above in the pure selling case, the vendor decides the second-period selling price,
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taking the leasing price and the first-period selling price as given. After determining the optimal second-period price, the vendor seeks the optimal first-period selling price by maximizing his total discounted profit. We use numerical examples to show the comparisons among the three licensing policies. We can observe that the hybrid strategy is a dominant strategy: the software vendor gains the highest profit under that strategy, and pure selling generates the lowest profits. Network externalities increase consumer utility and thereby their willingness to pay. However, the software vendor needs to be able to commit to future prices in order to reap the benefit created by this network effect. When consumers expect the vendor to lower prices in period 2, they will take advantage of the ability to defer their purchase. This choice hurts the vendor’s profit but benefits the consumers. Under the hybrid policy, the constraint that the upgrade price cannot exceed the price of the second version of the software is eliminated, so the profit of the hybrid equilibrium falls between the profit from leasing and that from selling.
4.
Conclusions
Software vendors now can use the Web to deliver software as a service based on a subscription model. The Coase Conjecture for traditional durable goods markets, as well as recent speculations of SaaS proponents, predict that Web-based subscription is likely to become the dominant means of licensing the durable good as software. We, however, find a limitation of this leasing type of licensing strategy—that is, leasing limits consumers’ flexibility in paying for upgrades in the future and therefore precludes some medium-value consumers from using the software. This limitation does not attract much attention in studies of traditional durable goods, because it is dominated by the benefit of leasing in helping the vendor commit to future prices. In the presence of a pronounced network effect, however, a software vendor must devote more effort in building up the installed base. The limitations of leasing become so evident when the network effect is strong that the vendor may choose a hybrid strategy over the pure subscription model. The results also contribute to the literature on durable goods. Software differs from conventional durable goods because of the low marginal production cost, network externalities in its distribution, the ease of upgrades, and strict intellectual property protection. Our paper customizes traditional approaches to durable goods research by solving the licensing selection problem faced by a software vendor. We show that with the existence of the effect of network externalities, the standard Coase Conjecture that “leasing creates the same profit to a monopolist durable good seller as that to a monopolist nondurable good seller” may not hold. Our findings should also help users realize the benefits and costs of each of the licensing policies and help them make the best decision taking into account of their own characteristics, product upgrades, and network 14
effects. A subscription model smoothes out cash payments and replaces a single lump sum with a per-period payment, which may be attractive when software budgets are limited. Consumers need to consider the total cost of ownership when evaluating their options, however, as our stylized model reveals that subscribing to software actually costs more than purchasing it.
Selected References Bhargava, H., and V. Choudhary ( 2001), “Information Goods and Vertical Differentiation”, Journal of Management Information Systems, 18, 2, 85-102. Brynjolfsson, E. and C. Kemerer (1996). “Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market,” Management Science, 42, 1627-1647. Choudhary, V. (2007), “Software as a Service: Implications for Investment in Software Development”, Proceedings of the 40th Hawaii International Conference on System Sciences (HICSS), Waikoloa, Hawaii. Choudhary, V., K. Tomak, and A. Chaturvedi (1998), “Economic Benefits of Renting Software,” Journal of Organizational Computing and Electronic Commerce 8(4), 277-305. Coase, R. (1972), “Durable-Goods Monopolist,” Journal of Law and economics 15, 143-149. Ellison, G. and D. Fudenberg (2000), “The Neo-Luddite's Lament: Excessive Upgrades in the software Industry,” Rand Journal of Economics 31(2), 253-272. Fudenberg, D. and J. Tirole (1998), “Upgrades, Tradeins, and Buybacks,” RAND Journal of Economics 29(2), 235-258. Huang K.W. and A. Sundararajan (2005), “Pricing Models for On-Demand Computing,” Working Paper CeDER-05-26, Center for Digital Economy Research, Stern School of Business, New York University. Kornish, L. J. (2001) “Pricing for a Durable-Goods Monopolist under Rapid Sequential Innovation,” Management Science 44 (11) 1552-1561. Seidmann, A. and D. Ma (2004), “ASPs versus Enterprise Software Solutions,” Workshop on Information Systems and Economics.
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