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.
2
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
3
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?
4
•
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
5
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
6
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 orderof-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 precommitment is a means of credible claim-staking to make competition more orderly.
7
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 latemover 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) gametheoretic 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
8
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 c i ( t ) = c i e
−t
.
9
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
(
)
1+r
timing when it pre-commits to be the Leader (i.e., ci / c j > (1 + r) /(2 −1)
2
−1/ r
). 3
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.
10
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) precommitment.
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.
11
Table 1. Functionality Addition Model Structures
MODEL
STRUCTURE
Costs
Latent Value
Assumptions
Propositions
BASELINE ANALYSIS HETEROGENEOUS VALUE
(BASE MODEL)
LATENCY (HVL MODEL)
Heterogeneous in both models
Homogeneous
Heterogeneous
1, 2 + 3a, 4
1, 2 + 3b, 4
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,
π (t , t ) =
L
i
L
i
π iL :
t Fj + δ
F
j
∫
ve
t iL + δ
− rt
∞
dt +
− rt
− rt
L
∫ ( v / 2 ) e dt − c i ( t i ) e i
π (t ) =
F
i
j ≠ i ; i ={A, B}
(1)
j ≠ i ; i ={A, B}
(2)
t Fj + δ
The Follower adopts later, and will earn,
F
i
L
π iF
∞
− rt
− rt
F
∫ (v / 2 )e dt − ci (ti ) e i
F
t iF + δ
(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,
tiL + δ . 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
F
market share, t j + δ , from its own subsequent entry. Thereafter, both gain v/2, a balanced split of
12
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, c i ( t iL ) and c i ( t iF ) , 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.
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 τ
L
A
by maximizing its Leader profit, by taking the Follower’s optimal entry timing τ BF
as given. Similarly, when Firm A decides to be the Follower, and Firm B enters first, then Firm A’s
entry timing
τ AF will maximize its profits as the Follower.
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.
13
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,
t iLF
, 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 t iLF must satisfy
π iL (tiL ,τ Fj ) = π iF (τ iF ) . Entry at t iLF involves non-negative profit, but it is earlier than the entry
time when Firm i is able to achieve maximum profit as Leader.
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, t ALF and tBLF . By applying
14
π iL (tiL ,τ Fj ) = π iF (τ iF )
under which i={A,B} and identifying t ALF and tBLF , we obtain the
relationships that are graphed in Figure 2, which we explain below.
Figure 2. Optimal Entry w/ Heterogeneous Entry Cost
.
At t BLF 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 t ALF , Firm B will have an incentive to do
so even earlier as the Leader, due to π BL (t ALF ,τ AF ) > π BF (τ BF ) . Since the Leader’s profit function,
π iL (t iL ,τ Fj ) , increases over time on the left of τiL , Firm B would like to delay entry, but only until t ALF ,
to retain leadership. In contrast, Firm A will not wish to be a Leader before t ALF 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
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
LF
cost firm, Firm B, enters at time t A
( t ALF < τ BL ) and the higher entry cost firm, Firm A,
enters at time τ A . (A proof is available from the authors.)
F
This proposition states that although the low-entry cost firm, Firm B, can gain the maximum
profit at time, τ BL , the high-entry cost firm, Firm A, can enter the market a little earlier than τ B as the
L
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 τ BL is not credible. So the firms have incentives to enter a little earlier to
LF
get Leader profits until t A . 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
L L F
earlier than t ALF . This is because π B (tB ,τ A ) is monotonically increasing over (0, τ AL ).
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.
16
As a traditional e-mail service provider though, Yahoo! should be a cost-leader in mass-storage email 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 δ A ≠ δ B , 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:
t Fj + δ j
π (t , t ) =
L
i
L
i
F
j
∫ ve dt +
− rt
t iL + δ i
∞
− rt
− rt
L
∫ (v / 2)e dt − c L (ti )e i
L
(3)
t Fj + δ j
for i={A,B}, and j ≠ i . The Follower’s profit is based on its entry timing, but with the difference in
value latency:
17
π
∞
F
i
(t ) =
F
i
∫
F
( v / 2 ) e − rt dt − c F ( t iF ) e − rt i
(4)
t iF + δ i
for i={A, B}. To solve for optimal entry timing for each firm, we compute the indifference entry
timing of each, by applying π i (ti ,τ j ) = π i (τ i ) . Specifically, t ALF must satisfy
L
L
F
F
F
π AL (tALF ,τ BF ) = π AF (τ AF ) and t BLF must satisfy π BL (tBLF ,τ AF ) = π BF (τ BF ) . 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<δA,
B
0<D1<D2, and Firm B will enter at t ALF and Firm A will enter at τ AF in equi-librium. When
δB>δA, there will be values of δA and δB , such that Firm A will preempt Firm B at time t BLF ,
B
and Firm B will enter at τ BF . When δB>δA, there also are values of δB and δA, Firm B will
B
B
still preempt Firm A at time t ALF and Firm A will enter at τ AF . (Proof also available from the
authors.)
In this proposition, D1 = πB (τ B ) −π A (τ A ) measures the difference in optimal Follower profit
F
F
F
F
L L
F
L L
F
between the firms, and D2 = π B (t B ,τ A ) − π A (t A ,τ B ) measures the difference in Leader profit
between Firm A and Firm B given the optimal entry of the Follower. Although Firm B is a lowerentry 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
18
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, t BLF , later than Firm
A’s, t ALF . Thus, Firm B will not prefer to lead until t BLF . However, Firm A will enter just before t BLF 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.
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 servicebased 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 technologybased 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).
REFERENCES
Au, Y. A., and Kauffman. R. J. Should we wait? Network externalities, compatibility, and electronic
billing adoption. JMIS (18:2), 2001, 47–63.
Au, Y. A., and Kauffman. R. J. Rational expectations and IT adoption. IS E-Bus. Mgmt (3:1), 2005,
1-24.
Balcer, Y., and Lippman, S.A. Technological expectations and adoption of improved technology. J.
Econ. Theory (34:2), 1984, 292-318.
25
Bayus, B., Jain S., and Rao, A. Too little, too early: Introduction timing and new product
performance in the personal digital assistant industry. J. Mktg. Res., (34:1), 1997, 50-63.
BBC News UK Edition. China blocks Google news site, November 30, 2004.
Chatterjee, R., and Sugita Y. New product introduction under demand uncertainty in compet-itive
industries. Mgrl. Dec. Econ. (11:1), 1990, 1-12.
Corante, C. In Google we trust. Microcontent News, March 24, 2005.
David, P. The dynamo and the computer: An historical perspective on the modern productivity
paradox, Amer. Econ. Rev. (80:2), 1990, 355-361.
Devaraj, S., and Kohli, R. Performance impacts of IT: Is actual usage the missing link? Mgmt. Sci.
(49:3), 2003, 273-289.
Dutta, P.K., Lach, S. and Rustichini, A. Better late than early: Vertical differentiation in the adoption
of a new technology. J. Econ. Mgmt. Strat. (4:4), 1995, 563-589.
Evers, J. and Perez, J. C. Google desktop search sparks surge of releases from competitors.
ComputerWeekly.com, December 14, 2004.
Ewalt, D.M. Google’s big bet: beyond instant messaging. Forbes.com, August 24, 2005.
Farzin, Y. H., Huisman, K. J. M. and Kort, P. M. Optimal timing of technology adoption. Journal of
Econ. Dyn. Contr. (22:5), 1998, 779-799.
Fudenberg, D. and Tirole, J. Preemption and rent equalization in the adoption of new technology.
Rev. Econ. Stud. (52:3), 1985, 383-401.
Goh, K.H. and Kauffman, R.J. Towards a theory of value latency for IT investments. In R. Sprague
(Ed.), Proc. 38th HICSS, Kona, HI, 2005.
Graham, J. Google expands search to desktop. The search wars have moved to the next dimension:
your computer desktop. USA Today, October 14, 2004.
Hellweg, E. Google’s Gmail ads hit the mark. Business 2.0, September 20, 2004.
Hitt, L.M., Wu, D.J. and Zhou, X. Investment in enterprise resource planning: Business impact and
productivity measures. JMIS (19:1), 2002, 71-98.
Hoppe, H.C. The timing of new technology adoption: theoretical models and empirical evidence.
Manchester School (70:1), 2002, 56-7.
Hoppe, H. C. and Lehmann-Grube, U. Second-mover advantages in dynamic quality competition. J.
Econ. Mgmt. Strat. (10:3), 2001a, 419-433.
Hoppe, H.C. and Lehmann-Grube, U. Second-mover advantages in technological competition.
Working paper, Univ. Hamburg, Germany, 2001b.
Ireland, N. and Stoneman, P. Technological diffusion, expectations and welfare. Oxford Econ. Pap.,
(38:2), 1986, 283-304.
Jensen, R. Adoption and diffusion of an innovation of uncertain profitability. J. Econ. Theory (27:1),
1982, 182-193.
Jovanovic, B. and Lach, S. Entry, exit, and diffusion with learning by doing. Amer. Econ. Rev.
26
(79:4), 1989, 690-699.
Kauffman, R.J. and Li, X. Technology competition and optimal investment timing: A real options
model. IEEE Trans. Eng. Mgmt., 52(1), 2005, 15-29.
Kauffman, R.J. and Mohtadi, H. Proprietary and open systems adoption: A risk-augmented
transactions cost perspective. J. Mgmt. Info. Sys., (21:1), 2004, 137-166.
Krim, J. EBay to acquire Internet phone leader. Washingtonpost.com, September 13, 2005.
Latif, U. Webmail wars. Techuser.Net, November 13, 2004.
Lieberman, M.B. and Montgomery, D.B. First-mover advantages. Strat. Mgmt. J. (9), 1988, 41-58.
Liedtke, M. Yahoo turns to startup to expand into desktop search,” USA Today, December 10, 2004.
Mariotti, M. Unused innovations. Econ. Letters, (38:3), 1992, 367-371.
McCardle, K.F. Information acquisition and the adoption of new technology. Mgmt. Sci. (31:11),
1985, 1372-1389.
McClelland, C. Google desktop search beta. ZDnet UK, October 15, 2004.
Miyagiwa, K., and Ohno, Y. Closing the technology gap under protection. Amer. Econ. Rev. (85:4),
1995, 755-770.
Narasimhan C., and Zhang Z.J. Market entry strategy under firm heterogeneity and asymmetric
payoffs. Mktg. Sci. (19:4), Fall 2000, 313-327.
Ord, R. Yahoo! Desktop Search superior to Google & MSN. Webpronews.com, January 11, 2005.
People’s Daily Online English. Yahoo launches 1G storage in China. July 29, 2004.
Reinganum, J. F. On the diffusion of new technology: A game theoretic approach. Rev. Econ. Stud.,
(48:3), 1981a, 395-405.
Reinganum, J. F. Market structure and the diffusion of new tech. Bell J. Econ. (12:2), 1981b, 618624.
Slashdot. The webmail wars. Internet discussion, slashdot.org/article.pl?sid=04/11/14/1357214,
accessed September 13, 2005.
Stenbacka, R, and Tombak, M. Strategic timing of adoption of new technologies under uncertainty.
Intl. J. Ind. Org. (12:3), 1994, 387-411.
Stoneman, P. and Ireland, N. The role of supply factors in the diffusion of new process technology.
Econ. J. (93), Suppl., Conf. Pap., 1983, 66-78.
Yoffie, D. B. Competing in the Age of Digital Convergence. HBS, Cambridge, MA, 1997.
27
INDIFFERENCE ENTRY TIMING ANALYSIS APPENDIX
If a firm views itself as a Follower, then the indifference entry timing, t iLF , is the earliest entry
time at which it has an incentive to reposition itself as a Leader. Applying π i (ti ,τ j ) = π i (τ i ) , we
L
obtain the indifference entry timing for firm i under the same value latency,
L
t iLF
F
F
F
,
by solving:
−r(tiLF+δ )
(v / r)e
−tiLF(1+r)
= cie
⎡⎛ c (1+ r) ⎞−r ⎛ cj (1+ r) ⎞−r ⎤ ⎛ c (1+ r) ⎞−(1+r)
+ (v / 2r)e ⎢⎜⎜ i −rδ ⎟⎟ + ⎜⎜
⎟ − ci ⎜⎜ i −rδ ⎟⎟
−rδ ⎟ ⎥
v
e
v
e
(
/
2
)
(
/
2
)
⎠ ⎝
⎠ ⎥⎦ ⎝ (v / 2)e ⎠
⎢⎣⎝
−rδ
for i = {A,B}, j ≠ i
When value latencies differ between firms ( δ A ≠ δ B ), t iLF should also satisfy
LF
π iL (tiL ,τ Fj ) = π iF (τ iF ) . Specifically, t i
solves:
−r ⎤
⎡
−r
−(1+r)
−rδ j ⎛⎜ c j (1+r) ⎞⎟ ⎥ ⎛⎜ ci (1+r) ⎞⎟
−r(tiLF+δi )
−tiLF(1+r) v ⎢ −rδi ⎛⎜ ci (1+r) ⎞⎟
+e
=ce
+ ⎢e
(v / r)e
⎥ −c
⎜
⎜⎜
i
−rδi ⎟⎟
−rδ j ⎟⎟ ⎥ i ⎜⎜
−rδ ⎟⎟
2r ⎢
⎜
(v / 2)e
⎝ (v / 2)e
⎠
⎝ (v / 2)e i ⎠
⎝
⎠
⎣⎢
⎦⎥
for i = {A,B}, j ≠ i
28
MODELING NOTATION APPENDIX
PARAMETER
L, F
i and j
ci(t)
R
δi
v, (v/2)
π iL (tiL , t Fj ) , π iF (t iF )
DEFINITION
Leader, Follower firm
Firm i = {A, B}; Firm i’s rival j = {A, B} with j ≠ i
Entry cost for Firm i as a function of time t
Weighted average cost of capital
Value latency period for Firm i
Leader (Leader/Follower) payoff (Follower enters)
Profit for Firm i as a Leader, Follower
π iF (τ iF )
Maximum profit for Firm i as Follower
Firm i's entry timing as a Leader, Follower
Firm i's optimal entry timing as a Leader, Follower
Firm i’s indifference entry time, when it is indifferent to
Leader or Follower
Difference in maximum Followers’ profit for Firm A and B.
L
F
ti ,( ti )
τiL ,( τiF)
tiLF
F
F
F
F
D1 = π B (τ B ) − π A (τ A )
D2= πB (tB ,τ A ) −π A (tA ,τ B )
L
L
F
L
L
F
Difference in Leaders’ profit for Firm A and B, with optimal
entry by Follower