OPTIMAL TIMING FOR SOFTWARE FUNCTIONALITY ADDITIONS BY INTERNET PORTALS

Robert J. Kauffman

Director, MIS Research Center, and Professor and Chair rkauffman@csom.umn.edu

Ping Wu

Doctoral Program pwu1@csom.umn.edu

Information and Decision Science Department

Carlson School of Management, University of Minnesota Minneapolis, MN 55455

Last revised: October 6, 2005

Note: Forthcoming in R. Sprague (ed.), Proceedings of the 39th Hawaii International Conference on Systems Science, Kauai, HI, January 2006, IEEE Computing Society Press, Los Alamitos, CA, 2006. _________________________________________________________________________________

ABSTRACT

Managerial decisions regarding software functionality additions at Internet portals are interesting to senior managers involved in e-commerce. Portals can leverage software functionality additions to improve their sites’ capabilities to create value for the consumers and improve their profitability. Current examples include Internet portals’ launch of desktop search engines, new mass-storage email services, and implementation of instant messaging services. We present decision models for an Internet portal to determine the appropriate launch time for a software functionality addition. We assume the portals have heterogeneous costs and value latencies for market acceptance of their functionality additions. A portal’s decision to be a leader in adding functionality depends on its launch cost and the latency of the benefits flow. Low-cost portals may choose to follow, if revenues are sufficiently slow to materialize. If high-cost portals expect to achieve rapid acceptance of the capabilities associated with the new functionality, they will choose to lead. KEYWORDS: Economic analysis, e-marketing, functionality additions, market entry portals, timing, value latency, vendor strategies. ______________________________________________________________________________ Acknowledgments. We thank two anonymous reviewers from the Electronic Marketing Mini-Track at the 39th Hawaii International Conference on Systems Science, as well as the co-chairs, Arnold Kamis, Ajit Kambil, Marios Koufaris and Bruce Weinberg, for helpful comments on an earlier version of this work. The authors also thank Steven Polaski, Terrance Hurley, Kaz Miyagiwa and Fred Riggins. Rob Kauffman acknowledges the MIS Research Center for partial support.

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1. INTRODUCTION

For technology vendors, understanding how to time product launch to make new technology

adoption effective in the marketplace is critical (Hoppe, 2002). Firms often compete based on the

time at which they choose to enter a market or launch a product. The research to date has focused on

the inertial nature of the diffusion process, and what it takes to “prime the pump” of technology

adoption under uncertainty with strategic interactions. In this research, we will focus on another

important aspect: determining the optimal time to launch new technology-based functionality for

existing Internet portals. We will explore a specific setting: when there is a lag between the launch

of new technology-based functionality relative to when the first value flow occurs.

Google and Yahoo!, the Internet portals, provide an example of the kinds of software

functionality-based issues that we address, specifically those that increase a firm’s capabilities with

e-marketing. Yahoo! and Google have demonstrated an intense level of competition since 2002: each

added new functionality to its portal Web site at different times (Ord, 2005). One dimension of the

competition has occurred in the area of desktop search, where Google has held the lead in “pure”

technology terms. In October 2004, Google announced its launch of a beta version of Google

Desktop Search, which indexes and locates information on PC hard drives as quickly as Google does

Internet search (McClelland, 2004). Two months later, Yahoo announced its own client, Yahoo!

Desktop Search (Evers and Perez, 2004). Yahoo! also announced a partnership with X1

Technologies, which has leap-frogged into the lead among search engines (Liedtke, 2004).

Yahoo!’s urgency to jump into desktop search is indicative of how quickly the functionality

competition is evolving. In a recent article published in the blog, Microcontent News (Corante,

2005), the author comments that “[a]lthough both [Yahoo! and Google] view their missions as

organizing the world’s information and making it accessible, they approach the task with vastly

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different stances.” Google, which rose to fame on the basis of innovative search algorithms and

related technology, has become the de facto leader in search engine services (Graham, 2004). But

now its aim is to grow beyond that, into the space that Yahoo!, MSN and other portals have

successfully occupied (Ord, 2005). Yahoo!, in contrast, is first and foremost a media company.

Search on Yahoo! has focused on topical links. But lately a greater proportion of its search services

have an embedded advertising and fee-based component. Regardless of firms’ past business models

and technological infrastructure, they have been drawn together into competition that is based on

digital convergence (Yoffie, 1997) among a number of technology capabilities. Google and Yahoo!

are exploring options based on collecting, organizing and making information accessible for people.

Managerial decisions about software functionality additions have been made with digital

convergence in mind. For Internet portals, this has been seen as e-mail services and online storage

space capabilities are increasingly provided by the same firms on the Internet, especially Google and

Yahoo! Until late 2003, however, most portals viewed email storage space either on a give-away

basis for small amounts of storage, or as a modest revenue generator for somewhat larger amounts of

storage. No firm approached storage as a potentially major driver of their capabilities—until April

2004. Then, Google launched Gmail, a Google-indexed email client with one gigabyte of free

storage, preempting any similar move by Yahoo! (Latif, 2004; Slashdot, 2004). Several months later

in July 2004, Yahoo! announced that it was launching a similar large-capacity e-mail service in

China (People’s Daily Online English, 2004), where Google had experienced difficulties with

government censorship due to some of its Web content (BBC News UK Edition, 2004).

A number of research questions arise that relate to product/service design for e-commerce.

• How do the costs of implementing new functionality affect an Internet portal’s timing

decision for market entry with the expanded functionality?

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• How will lag time to the appropriation of benefits in the market affect a portal’s decision to

add functionality? Can we provide normative guidance for managerial decision making on the

basis of a model that captures the latency of value flows?

• What will be the strategic interaction between a lower-cost and a higher-cost portal? Will the

lower-cost portal always choose to lead?

The available literature does not provide normative guidance for Internet portals to optimize the

timing of software functionality additions. We model this setting and the tradeoffs that are involved

to establish a new basis for doing this. We draw upon literature in Marketing, Economics and IS to

develop a model for decision-making with respect to functionality additions and entry strategies for

Internet-based products and services.

In the next section, we will lay out the theoretical background. The following two sections

present a model which captures the essence of the relationships and trade-offs involved in timing

decisions about functionality additions in the presence of heterogeneous entry costs, and different

value flow lags. We next present a managerial discussion of the results, and then conclude with an

overview of our primary contributions and the limitations of this research.

2. LITERATURE

Three areas of the literature support the development of a strategic timing model for functionality

additions by Internet portals. They are: the IS literature for its representation of IT value and value

latency, and vendor strategies for technology products adoption; Marketing for market entry strategy

constructs; and Economics for technology adoption timing modeling.

2.1. Vendor Adoption Strategies and IT Value Latency

Recent IS research on technology product adoption emphasizes the effect of network

externalities, technology uncertainty, and standards and technology competition. For example, Au

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and Kauffman (2001) show that firms which adopt technology react to expectations of network

externalities, and are inclined to wait for the uncertainties to be resolved, even when next generation

solutions are perceived to be better. Project or product value migrate in sync with changes in the

perceived value of an underlying technology standard (Kauffman and Li, 2005). Firms will defer

technology adoption until its likelihood to become a standard crosses a critical “threshold” in the

perceptions of the market. Technology adoption also will be affected by unexpected “shocks” that

cause managerial uncertainty in the marketplace (Kauffman and Mohtadi, 2004).

The concept of IT value latency for heterogeneous firms, however, has not been extensively

explored for technology adoption, especially its effect on adoption timing. IT value latency is

defined as the lag in business value returns after the implementation of a new IT investment (Goh

and Kauffman, 2005). IT value latency arising from the addition of new software functionality by

Internet portals is a critical driver of strategic timing decisions. We know from prior research (e.g.,

Devaraj and Kohli, 2003) that IT value may not be realized immediately after investment. Instead,

there are value lags that often affect when firms can fully appropriate value (via revenues or

profitability, for example). David (1990) argues that industry structures matter in terms of the ability

of firms to successfully appropriate value from their technology investments. Thus, we expect that a

given competitive industry setting (e.g., Wall Street financial services, or Internet-based selling) is

able to achieve more or less value appropriation by its firms, depending on a relevant set of

environmental factors.

The literature also suggests that IT investment performance and business processes are linked.

So, to maximize IT value, firms need to structure their business models and processes to take the

fullest advantage of their technology investment capabilities (Hitt, et al., 2002). We also observe that

there are differences among Internet portals and their capabilities for e-marketing. These firm-level

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heterogeneities occur as different adoption costs, different strategies, different managerial

capabilities, different technology infrastructures, etc., and are likely to cause these firms to exhibit

diverse choices for entry timing for similar technology-based functionality additions. We expect this

to occur when the firms have similar access to the new technology.

2.2. Product Launch and Market Entry

Research in Marketing has investigated optimal timing strategy for technology product launch

and market entry. But, there is less guidance on how firm characteristics affect the outcomes

(Lieberman and Montgomery, 1988). Three exceptions are Narasimhan and Zhang (200), Chatterjee

and Sugita (1990) and Bayus, et al. (1997). Narasimhan and Zhang (2000) model a firm’s market

entry decision under market uncertainty, firm heterogeneity, competition, cannibalization, and order-

of-entry effects. However, they only permit firms to make simultaneous decisions to enter in the first

period. We allow firms to enter at any period. This is a better description of actual business practices.

Chatterjee and Sugita (1990) analyze optimal timing strategy for irreversible investments in new

products under a symmetric duopoly market structure. Different from our paper, their model does not

permit firm heterogeneity in adoption cost structures.

Another related issue is time-to-market. Bayus, et al. (1997) further included firm heterogeneity

in development costs and their estimates of market size to study the optimal timing to launch new

products. They assume each firm pre-commits to an introduction date and product.1 However,

strategic interactions between firms decrease the viability of value-maximizing pre-commitments,

especially if firms believe that their rivals will gain a first-mover advantage from preemption. We

1 Pre-commitment and pre-announcement are key concepts in this work that we will use interchangeably. Firms pre-commit or pre-announce their strategic plans as a means to pre-empt and dissuade their competitors from taking similar actions. We see this most often in software with “vaporware,” but in many settings pre-commitment is a means of credible claim-staking to make competition more orderly.

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structure the decision process for a firm to analyze the optimal timing for new functionality launch

without pre-commitments.

2.3. The Economics of Technology Adoption Timing

Earlier works on technology adoption decision making in Economics provide theoretical

justification for the factors that affect this decision, including uncertain adoption profits, strategic

interactions, and demand and supply-side forces (Hoppe, 2002). Facing uncertain profitability, firms

can benefit by acquiring information before they take action (Jensen, 1982; McCardle, 1985). They

also can benefit by observing the adoption experience of earlier adopters (Mariotti, 1992), and market

reactions to new technology standards (Balcer and Lippman, 1984; Farzin, et al., 1998). Clearly,

waiting to adopt can pay off. Other work focuses on strategic interactions among firms in the product

market. For example, a firm’s decision to adopt depends on the tradeoff between early and late-

mover advantage (Reinganum, 1981a; Fudenberg and Tirole, 1985; Dutta, et al., 1995; Hoppe and

Lehmann-Grube, 2001a, 2001b). Demand and supply-side forces also drive technology adoption

decisions. Inertial adoption may be due to expected declines in suppliers’ costs of producing new

technologies (Stonemand and Ireland, 1983; Ireland and Stoneman, 1986) and the reduced

production costs of learning-by-doing (Jovanovic and Lach, 1989).

The seminal contribution on adoption timing under rivalry is Reinganum’s (1981a, 1981b) game-

theoretic approach, which specifies a timing game in which both firms pre-commit their entry timing.

Fudenberg and Tirole (1985) extend this analysis by allowing firms to preempt. They demonstrate

that first-mover advantage is not supported by sub-game perfect equilibrium strategies if firms are

unable to pre-commit to future actions. Stenbacka and Tombak (1994) further introduce uncertainty

into the timing of new technology adoption. A higher level of uncertainty increases the extent of

dispersion for the equilibrium of adoption timing among firms. Miyagiwa and Ohno (1995) further

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consider declining adoption cost over time, and show why firms have an incentive to delay adoption.

However, this research views firms as homogeneous or heterogeneous only in terms of their adoption

costs. We will assume that firms are heterogeneous not only in adoption costs and but also in their

time lags to appropriate technology adoption profits.

3. MODELING SOFTWARE FUNCTIONALITY ADDITIONS

We extend Fudenberg and Tirole’s (1985) new product entry game to an environment where

firms face heterogeneous adoption costs that decline over time, and different value latencies for the

market acceptance of their software functionality additions. Optimal timing of functionality additions

depends on the interplay between costs and benefits, adjusted for when they occur. Most Internet

portals—and firms, in general—have an incentive to add specific functionality before their

competitors do. But still, firms may prefer to wait, and not enter a new market immediately, while

entry cost declines and various market uncertainties are resolved (Au and Kauffman, 2001 and 2005).

3.1. Preliminary Assumptions

Consider a duopoly market in which two firms, such as Yahoo! (Firm A) and Google (Firm B),

are competing in a new segment called desktop search, as we described earlier. Both firms can

decide whether to enter the market at any time t by launching new desktop search functionality. In

each period, firms simultaneously choose strategies: {adopt, not adopt}. The implementation cost of

adding desktop search functionality is ci(t), an entry cost for firm i at time t, and r is the firm’s

weighted average cost of capital. (See the Modeling Notation Appendix.) We next specify several

assumptions:

• A1 (Declining Entry Cost Assumption). For each firm, the entry cost of adding new

functionality is a one-time cost which declines over time at a declining rate. We assume that

the entry cost is . tii ectc −=)(

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With the rapid development of technology, the fixed cost of adding new functionality is likely to

decline because of standardization, reduced technical uncertainty and temporally-significant

technology breakthroughs. 2 Firms that delay adding new functionality will face lower entry costs

than firms that do not. But waiting to enter will cause firms to lose the chance to earn monopoly

profits in the market, since others will likely enter first. Our first assumption creates an incentive for

the firm to delay adoption. If firms’ entry costs do not declining over time, then all firms would

choose to adopt as soon as possible; the profitability associated with adoption would decrease over

time.

• A2 (Unequal Entry Cost Assumption). The entry cost of Firm A is higher than Firm B’s,

but their entry costs decline at the same rate over time.

• A3a (Homogeneous Value Latency Assumption). Each firm’s payoff from adding new

functionality is delayed for a value latency period, and both firms have the same value

latency periods.

• A3b (Heterogeneous Value Latency Assumption). Each firm’s payoff from adding new

functionality is delayed for a different value latency period.

• A4 (Entering Firms’ Cost Ratio Assumption). The cost ratios for Firm i and j are large

enough so that Firm i has an incentive to be a Leader at an earlier date than its optimal

timing when it pre-commits to be the Leader (i.e., ( ) rrji rcc /11 )12/()1(/ −+ −+> ). 3

2 Using an entry cost that declines at the same rate for all firms is a pragmatic modeling assumption. In the real world, one will be unlikely to observe this, in much the same way as using “proxy variables” in empirical research is not a perfect representation to achieve effective empirical explanations. Nevertheless, approaches involving simplification enable us to develop basic results and managerial insights for the optimal timing issues studied here. 3 The extent to which large cost ratios between firms will be observed is an empirical question. We expect them to occur particularly when one firm has much less experience in a given area of software functionality or when one firm is larger.

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When the Entering Firms’ Cost Ratio Assumption is satisfied, Firm i will benefit from entering

earlier than it would if it had pre-committed to being the first mover. This focuses on just those firms

which have large cost ratios so that one firm has an incentive to adopt the new functionality early. It

also focuses our analysis on cases under which entry timing becomes more vital for firms’

profitability without requiring an unrealistic assumption of entry role (i.e., Leader or Follower) pre-

commitment.

We also assume that each firm can observe its competitor’s entry timing, cost of entry, and value

latency period—even if the timing of value latency is different across firms. Au and Kauffman

(2005) argued that firms will “share private information about their costs, or even signal to other

firms their interest” if they want to obtain the potential benefits from network externalities. When it

comes to entry with software functionality additions by Internet portals, we expect that most of the

decisions will involve relatively mature technology (e.g., used elsewhere already, relatively well

known costs and benefits, etc.). As a result, firms will have good estimates about their own entry

costs and value latency periods. The Internet portals have existed long enough to know about

competitors’ operating characteristics, including their entry costs and value latency. 4

3.2. Modeling Setup

We begin with a Baseline Analysis Model (Base Model) in which both firms have different entry

costs but similar value latency periods. Neither firm needs to pre-commit to be a Leader or a

Follower. The Heterogeneous Value Latency Model (HVL Model) further relaxes the assumption that

both firms have to wait a similar time before value flows occur. (See Table 1.)

4 Assuming complete information, as we do here, may seem restrictive. This is typical in economic modeling research to establish a “base case” though, in spite of the fact that it is only an approximation of the real world.

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Table 1. Functionality Addition Model Structures

MODEL STRUCTURE

BASELINE ANALYSIS (BASE MODEL)

HETEROGENEOUS VALUE LATENCY (HVL MODEL)

Costs Heterogeneous in both models Latent Value Homogeneous Heterogeneous Assumptions 1, 2 + 3a, 4 1, 2 + 3b, 4 Propositions 1 2

3.3. The Baseline Analysis Model (Base Model)

Leader and Follower Profits. The profit function is based on the entry timing of a Leader (L)

and a Follower (F), which can be either of Firm A or B. If the firm adopts the technology earlier, it

will earn a Leader’s profit, : Liπ

j ; (1) Li

Fj

Fj

Li

rtLii

t

rtt

t

rtFj

Li

Li etcdtevdtvett −

∞

+

−+

+

− −+= ∫∫ )()2/(),(δ

δ

δ

π i≠ },{ BAi=

The Follower adopts later, and will earn, Fiπ

j ; (2) Fi

Fi

rtFii

t

rtFi

Fi etcdtevt −

∞

+

−∫ −= )()2/()(δ

π i≠ },{ BAi=

(Again, see the Modeling Notation Appendix.)

The first term in Eq. 1 is the present value of the Leader’s payoff ν after its value latency period,

. δ+Lit δ indicates the amount of time after entry it takes to obtain a return on the new software

functionality. The Leader earns this until the Follower begins to generate revenue and capture

market share, , from its own subsequent entry. Thereafter, both gain v/2, a balanced split of δ+Fjt

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value. 5 This is reflected in the second term for Leader and the first term for the Follower. Their

final terms capture the present value of one-time entry costs, and , which decline over

time. Note that the Follower does not earn anything until after it adopts the new functionality, when

value appropriation begins. So, in Eq. 1, the first term in Leader’s profit function will never be

obtained by the Follower: it earns no monopoly profit by delaying its adoption.

)( Lii tc )( F

ii tc

Entry Analysis. Solution of the model for optimal entry timing involves the recognition that each

firm is able to calculate its optimal timing, assuming its Leader or Follower role, by maximizing the

related profit function. So if Firm A chooses to enter first, then it will choose the Leader’s optimal

entry timing by maximizing its Leader profit, by taking the Follower’s optimal entry timing

as given. Similarly, when Firm A decides to be the Follower, and Firm B enters first, then Firm A’s

entry timing will maximize its profits as the Follower.

LAτ F

Bτ

FAτ

Real world observation of the Internet portals, Yahoo! and Google, related to the addition of

desktop search and large capacity e-mail services, shows their flexibility in taking on whatever role is

most profitable for them. For example, Yahoo! implemented an instant message service very early

on, but it is only recently that we see Google beginning to explore this—in spite of the fact that such

a service aligns well with Google’s efforts with mass-storage email. So Yahoo! and Google may not

pre-commit to their roles in adding new functionality.

Pre-announcing its role will not be credible if the Follower can benefit from preempting the

Leader. Yahoo! and Google did not pre-commit to leading or following in functionality additions that

support e-marketing via their portals. Instead, they have been flexibly taking on whatever role is 5 The use of v/2 to represent the post-entry Follower-Leader profit split is a reasonable first approximation, and is similar to the classic Nash bargaining game literature, where we see firms split value that is left over from failed contracts. firms are high-cost and low-cost relative to one another. An extension could involve balanced post-entry value split and unbalanced post-entry value split analysis—the firms’ split is equal or it is not. This will cover every possible case, unlike v/2.

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most profitable for them, and jumping from being a Follower to being a Leader if the profit from

preempting is higher than from being preempted.

We define indifference entry timing, , as the earliest time at which firms will be indifferent

between choosing between being a Leader or a Follower in adding portal software functionality. (See

the Indifference Entry Timing Appendix.) Figure 1 illustrates indifference entry timing as the first

time when a Leader’s profit function equals its maximum profit as a Follower. So must satisfy

LFit

LFit

)(),( Fi

Fi

Fj

Li

Li t τπτπ = . Entry at involves non-negative profit, but it is earlier than the entry

time when Firm i is able to achieve maximum profit as Leader.

LFit

Figure 1. Indifference and Optimal Entry Timing

Note: The Follower’s maximum profit is unaffected by the Leader’s entry (flat line). But, the Leader’s profit is affected by its own entry time (curved line) and the Follower’s entry time.

Based on the Unequal Entry Cost Assumption, firms differ in their entry cost structures. Higher

entry costs may be due to managerial inexperience, limitations with the firm’s absorptive capacity,

and infrastructure weaknesses that affect the adoption process. Heterogeneous costs for entry result

in different indifference entry timing for Firm A and B, and . By applying LF LFAt Bt

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)(),( iijii t τπτπ = FFFLL under which i={A,B} and identifying and , we obtain the

relationships that are graphed in Figure 2, which we explain below.

LFAt

LFBt

Figure 2. Optimal Entry w/ Heterogeneous Entry Cost

.

At in Figure 2, Firm A prefers to follow Firm B with the deployment of new portal

functionality because the profit for Firm A to follow is higher than for Firm A to preempt Firm B.

Conversely, if Firm A enters the new product market at time , Firm B will have an incentive to do

so even earlier as the Leader, due to

LFBt

LFAt

)(),( FB

FB

FA

LFA

LB t τπτπ > . Since the Leader’s profit function,

, increases over time on the left of , Firm B would like to delay entry, but only until ,

to retain leadership. In contrast, Firm A will not wish to be a Leader before because it can obtain

a higher payoff by being a Follower. In other words, Firm A will not have an incentive to be a Leader

before its indifference entry timing because it can earn a higher payoff as a Follower by sharing the

market. Since Firm A’s entry cost is higher than Firm B’s, Firm A’s indifference entry timing will

be later than Firm B’s. Thus, Firm A cannot enter the market as a Leader: its earliest possible entry

timing is still later than Firm B’. In other words, Firm B should be a Follower, not a Leader. This

),( Fj

Li

Li t τπ L

iτLFAt

LFAt

15

leads to Proposition 1, which addresses optimal entry timing for two firms with the same value

latencies but different entry costs.

• Proposition 1 (Optimal Entry with Heterogeneous Entry Costs). When both firms have the

same value latencies, the subgame perfect equilibrium for market entry is that the lower entry

cost firm, Firm B, enters at time ( ) and the higher entry cost firm, Firm A,

enters at time . (A proof is available from the authors.)

LFAt L

BLFAt τ<

FAτ

This proposition states that although the low-entry cost firm, Firm B, can gain the maximum

profit at time, , the high-entry cost firm, Firm A, can enter the market a little earlier than as the

Leader and gain profit strictly higher than that for a Follower at the time. Thus, Firm B’s strategy of

entering as a Leader at time is not credible. So the firms have incentives to enter a little earlier to

get Leader profits until . Then, Firm A cannot earn more as a Leader than as a Follower.

However Firm B can obtain profit as a Leader, which is higher than what it could achieve at any time

earlier than . This is because is monotonically increasing over (0, ).

LBτ

LBτ

LBτ

LFAt

LFAt ),( F

ALB

LB t τπ L

Aτ

This proposition offers an interpretation of how Internet portals make decisions about when to

add desktop search functionality. Recall that Google launched the desktop search earlier than

Yahoo!. Google was better positioned to launch new search engine functionality at a lower cost than

Yahoo!, which wasn’t specialized in search functionality. So Yahoo! probably had a higher entry

cost. The first proposition suggests that being a Leader yielded more profit for Google than being a

Follower. So preempting was the appropriate strategy.

The same explanation, however, does not work as well in the interpretation of Google and

Yahoo!’s decision to add mass-storage e-mail services. We assumed in the proposition that both

firms have the same latency periods for the appropriation of value from mass-storage e-mail services.

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As a traditional e-mail service provider though, Yahoo! should be a cost-leader in mass-storage e-

mail services. Its experience and the lower entry cost should have positioned it well for this.

However, there may have been no additional value associated with expanding the current e-mail

capacity based on the number of new customers that Yahoo! could attract. This would have affected

the potential for click-through increases for advertisements on Yahoo!, since it already had many

users. Thus, Yahoo! may have expected a longer value latency before it would be able to realize the

value of mass-capacity e-mail service, compared to Google. Based on our analysis, it’s not easy to

determine if Yahoo! can maximize profitability by leading or following. Instead, this will depend on

the size of the related first and later-mover advantages, and what competitors can be expected to do.

Next, we extend our model to incorporate heterogeneous value latency and how it affects firms’ entry

timing decision.

4. HETEROGENEOUS VALUE LATENCY (HVL) MODEL

We have assumed that two competing Internet portals seek to maximize value by achieving the

best timing for their software functionality additions have the same value latency periods. We now

shift gears to use the Heterogeneous Value Latency (HVL) Assumption (A3b), with , to derive

the next proposition. First-mover monopoly profit and second-mover lower entry costs depend on

firm differences in value latency and entry costs, and the payoff functions will change. In our HVL

Model, a Leader’s profit is affected by the Follower:

BA δδ ≠

(3)

for i={A,B}, and j . The Follower’s profit is based on its entry timing, but with the difference in

value latency:

Li

jFj

jFj

iLi

rtLiL

t

rtt

t

rtFj

Li

Li etcdtevdtvett −

∞

+

−+

+

− −+= ∫∫ )()2/(),(δ

δ

δ

π

i≠

17

(4) Fi

iFi

rtFiF

t

rtFi

Fi etcdtevt −

∞

+

−∫ −= )()2/()(δ

π

for i={A, B}. To solve for optimal entry timing for each firm, we compute the indifference entry

timing of each, by applying )(),( Fi

Fi

Fj

Li

Li t τπτπ = . Specifically, must satisfy LF

At

)(),( FA

FA

FB

LFA

LA t τπτπ = and must satisfy LF

Bt )(),( FB

FB

FA

LFB

LB t τπτπ = . By comparing them, we are able

to identify whose entry timing as a Leader is more credible. Our next proposition states the condition

for optimal entry timing for both firms and their entry roles when both firms have different cost

structures and value latencies.

• Proposition 2 (Optimal Entry with Heterogeneous Value Latency). When δB<δB A,

0<D1<D2, and Firm B will enter at and Firm A will enter at in equi-librium. When

δ

LFAt

FAτ

BB>δA, there will be values of δA and δB , such that Firm A will preempt Firm B at time ,

and Firm B will enter at . When δ

LFBt

FBτ B>δB A, there also are values of δBB and δA, Firm B will

still preempt Firm A at time and Firm A will enter at . (Proof also available from the

authors.)

LFAt

FAτ

In this proposition, )()(1FA

FA

FB

FBD τπτπ −= measures the difference in optimal Follower profit

between the firms, and measures the difference in Leader profit

between Firm A and Firm B given the optimal entry of the Follower. Although Firm B is a lower-

entry cost firm, it may prefer to be a Follower when its value latency period is longer than a specific

duration. (See Figure 3). In this case, the higher-cost Firm A will preempt Firm B when the latter is

indifferent between being a Leader or a Follower. In contrast to the Base Model with homogeneous

value latency, Firm B will not be sure it should be the Leader or Follower in the HVL Model. A

),(),(2FB

LA

LA

FA

LB

LB ttD τπτπ −=

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decision to enter with new software functionality is sensitive to value latencies for both firms.

A decision to enter with new software functionality is sensitive to both firms’ value latencies. In

Figure 3, Firm B’s long value latency period pushes its indifference entry time, , later than Firm

A’s, . Thus, Firm B will not prefer to lead until . However, Firm A will enter just before to

gain a larger Leader’s profit. So the higher-entry cost Firm A will preempt Firm B when the latter is

indifferent between being a Leader or a Follower. Also, the lower-entry cost Firm B prefers to follow

at its optimal Follower entry timing, rather than to lead.

LFBt

LFAt

LFBt

LFBt

Figure 3. Lower-Entry Cost Firm Chooses to Follow

A higher-entry cost firm will preempt a lower-entry cost firm when its value latency period is

shorter than the lower-cost firm’s. The higher cost firm will choose to be a Follower if its value

latency is not sufficiently short compared to the lower- entry cost firm. (See Figure 4).

We think of Google as a higher-entry cost firm (compared to an e-mail-focused portal) which

benefited from leading the market with its GMail service. Google expected a dramatic rise in new

customers and higher ad click-through rates, resulting in rapid value appropriation in the form of

higher advertising revenue [17]. Google’s preemption advantage was its shorter value latency, which

may have dominated what Yahoo! could do in terms of new value generated then.

19

Figure 4. Lower-Entry Cost Firm’s Chooses to Lead

When firms compete intensively for the creation of business value from software functionality

additions on Internet portals, the optimal timing of entry will be critical to their success. A wise

entry timing decision balances the tradeoff between lower entry costs and higher value latency. Our

second proposition implies that firms’ capabilities to estimate their own and their competitors’ value

latency period will determine their ability to successfully choose the right entry timing. Because they

have heterogeneous value latency, either can be the first to add new functionality, depending on how

their value latency periods compare. The higher-entry cost firm may benefit from adding

functionality earlier. However, it could also benefit from being a Follower, if its value latency as a

lower-entry cost firm is short enough. Thus, the firm’s choice of entry is sensitive to the estimate of

its own and its competitor’s value latencies.

5. DISCUSSION

Competition in the marketplace encourages Internet portals to frequently expand the contents of

their service functionality to attract a larger number of diverse customers. We have observed this to

be true, for example, with Google’s and Yahoo! competition for desktop search and mass-storage e-

20

mail customers. Recently, this competition has extended to another new service area: voice over

Internet protocol (VoIP) services. eBay announced its acquisition of the global leader in Internet

telephone service, Skype Technologies SA (Krim, 2005). Skype, headquartered in Luxembourg and

founded by the entrepreneurs who created P2P file-sharer KaZaA, is an especially interesting move,

since it signals one of the potential next steps in the competition among e-auction portals. With its

acquisition, eBay will be able to move into Internet telecommunication, and be able to leverage

Skype’s capabilities to effectively support communication between auction buyers and sellers in its

native medium of the Internet, while enhancing its transaction-making auction mechanism.

However, eBay is not the only portal that has shown an interest of involving in Internet

telecommunication. Google, which has been aggressively transforming itself into a larger service-

based Internet portal, is also exploring a new strategy involving Internet communication, by releasing

an instant messaging voice chat program, Google Talk (www.google.com/talk/) (Ewalt, 2005).

Although both will soon roll out VoIP services, it will be interesting to see how these firms

compete with their nearest rivals. In this paper, we have provide some guidelines for these firms to

choose an appropriate time, in view of their rivals’ and their own capabilities to appropriate value

and their existing cost structures. Our modeling and theoretical arguments suggest that with the sort

of competition that is occurring relative to digital convergence on the Internet, firms should try to

optimize their entry timing, by analyzing their entry costs and lag times to payoff. In Google’s case,

as a traditional search engine portal, it has abundant technical and senior management strategic

planning capabilities to make adding VoIP services successful. However, it does not have direct

knowledge and experience related to VoIP services. In contrast, Skype should offer strong

complementarities with eBay—whether the services are intended to support its e-auction services or

21

will lead to another new business. eBay, as a result, is likely to have an absolute advantage for VoIP

entry cost.

In addition to entry costs, we also encourage the assessment of the value latency periods of

competing firms relative to the launch of Internet phone service, based on our Optimal Entry with

Heterogeneous Value Latency Proposition. Returning to the case of eBay’s acquisition of Skype, it

is appropriate to point out that the firm’s potential profit depends on the size of its actual and

potential customer base, the quality of the marketing and operational services strategies for VoIP,

and the alignment of the merger partners’ key business functions. By allowing buyers and sellers to

exchange information instantly about auction items, eBay is “perfecting” its transaction-making

auction market mechanism. This will lead to more informed trading, more effective buyer price

discovery and improved transactability. Overall, this should lead to an increased number of

successful auctions. With its large pool of buyers and sellers worldwide, we expect that eBay will

be able to monetize its investment in Skype very rapidly.

Google, by contrast, has not operated services for which Internet phones will directly lead to

profitability. Instead, Google’s users will enjoy an additional complementary service, similar to

what Yahoo! and MSN are already offering. Our prediction is that it will be somewhat more difficult

to monetize its instant messaging and talk services capabilities. This should lead to a longer value

latency period, and hold back for some time the value payoffs associated with adding this kind of

software functionality. Although these predictions are based on the various predictions of our simple

analytical model, they nevertheless are suggestive of its use. We expect eBay and Google to have

their most significant competitive interactions in the marketplace with similar kinds of firms—

electronic auction markets for eBay, and Internet portals for Google. They will do well in their own

competitive contexts to assess whether they are sufficiently low-entry cost and low-value latency to

22

benefit from acting as a market leader upon entry. Our modeling prescription is that the optimal

timing for a firm like eBay, which appears to be a lower-cost entrant, should enter at its competitor’s

indifference entry timing. A higher-cost entrant and competitor of eBay, then, would choose to adopt

according to optimal entry timing for a follower firm. This kind of decision-relevant information

provides useful guidance for Internet portals that seek a means to optimize their entry timing.

6. CONCLUSION

We analyzed how to optimize the timing for adding new functionality for Internet portals by

emphasizing the effects of entry costs and value latency for appropriating benefits in the market.

6.1. Main Findings

When firms optimize their choices to lead or follow relative to one another’s best strategic

actions, their timing decisions for adding new software functionality will depend mostly on the

tradeoff between first-mover advantage and their beliefs about the profitability of their rival’s timing

decision. The cost of adding new software functionality on a portal Web site becomes a main driver

of optimal timing when the competing firms face the same time lag for appropriating business value.

When two firms have the same value latency for entry, the one with the lower-entry cost will

benefit from preempting the higher-entry cost firm. Although we have not presented an analysis of

strategic pre-commitment to the Leader/Follower entry roles, on the basis of other analysis work we

have done, it appears that if the Leader doesn’t pre-commit, then it will add new software

functionality at an earlier date than if it pre-commits. Why? Because the firm will bear more cost

from being preempted when strategic interaction occurs. Heterogeneous value latency, in contrast,

allows a higher-cost firm to profit from adding new functionality earlier than the lower-cost firm,

23

when the expected value latency period for the lower-cost firm is sufficiently longer than that of the

higher cost firm. The value latency makes it possible for the higher-cost firm to enter first.

6.2. Managerial Implications

Although managers should expect to benefit from adding technology-based functionality if their

firms act as leaders and enter the market first, they should not necessarily expect the same benefits to

accrue if they face longer lag periods to realize functionality benefits than their rivals. In the context

that we have described—adding software functionality to Internet portals—managers should be able

to obtain good estimates of entry costs so that one of the main factors that affects functionality

addition timing can be quantified.

Our model demonstrates that it may be optimal for a firm with higher entry cost to be a leader in

adding a new functionality when its estimated value latency is sufficiently short. We predict, as a

result, that mixed empirical evidence regarding role choices for functionality addition competition

among the Internet portals should not be surprising to observe. Some examples illustrate this.

Yahoo! launched Yahoo! Finance earlier than MSN.com, which ultimately offered similar

capabilities. Google still has not done this though. We further observe that Google launched Google

Desktop Search and GMail earlier than Yahoo! launched similar functionality—which it eventually

did, of course. In these kinds of settings, optimal timing for adding new software functionality should

depend on a mix of entry cost and value latency. Thus, we remind managers that focusing too much

on competitive entry cost structures or reducing entry cost will be likely to lead to over-estimate

leaders’ benefits, resulting in earlier entry than is appropriate.

Although our results focus on the importance of finding the right entry timing for technology-

based functionality additions, managers will find a similar set of forces at work in other settings too.

The more general applications occur in settings that involve: adding new product lines; adopting new

24

technology standards that are embedded into products (e.g., Wi-Fi 802.11a and 802.11g, when

802.11b is already supported); and determining whether to join a network that offers different service

functionality levels compared to what is currently available. When managers face a decision related

to these kinds of impending changes, they will need to estimate their competitors’ entry costs and

value latency periods. This information will be useful in establishing the game plan for “entry

timing” for adding a new product line, adopting next-generation embedded standards, and obtaining

upgraded network service levels.

6.3. Limitations

This analysis has limitations. First, we considered the value latency for appropriating business

value from software functionality addition entry as a fixed value that can be known in advance. But

this assumption may not capture the true dynamic nature of technology value uncertainty. Value

uncertainty should be decreasing over time. Thus, an interesting extension might result from

permitting the latency of realizing software functionality addition value to decrease over time. We

expect the firm’s expected payoff will become more sensitive to entry timing. Second, our model

doesn’t include market competition. An extension of the model to duopoly competition will enable

senior managers of participating firms to consider not only the appropriate entry timing, but also the

extent of the product quantity that they offer (e.g., in terms of access to Google GMail, which

initially had only limited distribution to get the “buzz” going).

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27

INDIFFERENCE ENTRY TIMING ANALYSIS APPENDIX

If a firm views itself as a Follower, then the indifference entry timing, , is the earliest entry

time at which it has an incentive to reposition itself as a Leader. Applying

LFit

)(),( Fi

Fi

Fj

Li

Li t τπτπ = , we

obtain the indifference entry timing for firm i under the same value latency, ,

by solving:

LFit

)1()1()(

)2/()1(

)2/()1(

)2/()1()2/()/(

r

ri

i

r

rj

r

rirrt

itr

evrcc

evrc

evrcervecerv

LFi

LFi

+−

−

−

−

−

−−+−+−

⎟⎟⎠

⎞⎜⎜⎝

⎛ +−⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛ ++⎟⎟

⎠

⎞⎜⎜⎝

⎛ ++= δδδ

δδ

for i = {A,B}, j ≠ i

When value latencies differ between firms ( BA δδ ≠ ), should also satisfy LFit

)(),( Fi

Fi

Fj

Li

Li t τπτπ = . Specifically, solves:

LFit

)1(

)2/(

)1(

)2/(

)1(

)2/(

)1(2

)1()()/(

r

irev

ricic

r

jrev

rjcjre

r

irev

ricirervrLF

iteici

LFitr

erv

+−

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−+

−

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡ −

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−

+−+

−

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−+−

++−

=+−

δδδ

δδδ

for i = {A,B}, j ≠ i

28

MODELING NOTATION APPENDIX

PARAMETER DEFINITION L, F Leader, Follower firm i and j Firm i = {A, B}; Firm i’s rival j = {A, B} with j ≠ i ci(t) Entry cost for Firm i as a function of time t R Weighted average cost of capital δi Value latency period for Firm i v, (v/2) Leader (Leader/Follower) payoff (Follower enters)

),( Fj

Li

Li ttπ , )( F

iFi tπ Profit for Firm i as a Leader, Follower

)( Fi

Fi τπ Maximum profit for Firm i as Follower

tiL,( ti

F) Firm i's entry timing as a Leader, Follower τiL ,( τiF) Firm i's optimal entry timing as a Leader, Follower ti

LF Firm i’s indifference entry time, when it is indifferent to Leader or Follower

D1 = )()( FA

FA

FB

FB τπτπ − Difference in maximum Followers’ profit for Firm A and B.

D2= ),(),( FB

LA

LA

FA

LB

LB tt τπτπ − Difference in Leaders’ profit for Firm A and B, with optimal

entry by Follower