The model of this thesis follows the theoretical frameworks from Smit and Trigeorgis (2004). We implement the model to the laptop OEM/ODM industry with four-stage game under complicated market structures. Under the conditions of the price competition and the duopoly market, this study assumes that the first mover, the company which invests in the project first, can obtain 5 percent additional quantity when the market moves up and 3 percent additional quantity when the market moves down. This study uses the real options game methodology which considers the market uncertainty but deliberates Quanta’s competitors’ reactions including Compal Electronics, Inc., Wistron Corporation, and Inventec Corporation; the result demonstrates that the optimal decision of Quanta, the leader company of the industry, is to invest in the multi-touch panel laptop project in the first period (2008).
Besides, there are four vital results of the scenario analyses. First of all, investing in the project in the first period will be the optimal decision for Quanta when the market demand of the touch panel laptops is greater than 20,643,414.
Conversely, Quanta will choose to defer the project at the beginning when the market demand is less than 10,000,000.
Secondly, higher volatility has higher investment value and deferral value.
Quanta will decide not to invest the project if the volatility is less than 0.1.
Thirdly, no matter how the risk-free rate shifts, it can be found that investing in the project is the optimal decision for Quanta.
Finally, if the market moves up, Quanta which is the first mover, will choose to invest in the project when the additional quantity is more than three percent.
Accordingly, if the market moves down, investing in the project is still the optimal decision for Quanta when the additional quantity is less than three percent.
By the way, there are two recommendations that we can do for the future research. Firstly, the demand function of the Bertrand duopoly price competition model can be modified to fit the status of the laptop OEM/ODM market appropriately.
Secondly, the competiton in the laptop OEM/ODM market is fierce recently. For example, the total shipment of Wistron in the fourth quarter of 2008 is more than the shipment of Compal. Moreover, the total shipment of Compal in March of 2009 surpasses the total shipment of Quanta. We recommend that the decision tree be modified to a four-player game rather than a two-player game of this case.
Appendixes
Appendix 1
Derivation of Equilibrium Prices
We assume for simplicity that the demand for the laptops is linear in prices:
( , , )
i i j it it i j
Q P P θ = θ − b P + d P
(A.1) where the quantity which is sold by company is related to its price and the competitors’ price . Besides, The coefficients and ( , assuming
demand substitutes) capture the sensitive of the quantity sold to the firm’s own and its competitor’s price settings, respectively. The profits of each firm i (where = Quanta or Others) are given by to competitor price changes according to its reaction function. Substituting the expression for
R P in place of
i( j) in equation (A.1) gives the general asymmetricNash equilibrium price expression:
P
j, ,
choose the price that maximizes its own profit value, using the reaction function of the follower. Maximizing
V P R P
i( ,i j( ))i over Pi , givenR P
j( )i , gives a Stackelberg Taking the Stackelberg leader price into its competitor’s reaction functionR P
j( )i gives the Stackelberg follower price: single decision to be made, the sets of actions and pure strategies are identical.However, if there is more than one decision to be made, the action sets and pure strategies are no longer identical and there are now two. To distinguish between them,
we shall call one a “mixed strategy” and the other a “behavioural strategy.”
A mixed strategy δ specifies the probability ( )
p s with which each of the pure
strategies s∈S . Suppose the set of strategies isS
={ s s s
a, , then a mixed b, c, ...}
strategy can be represented as a vector of probabilities: δ =( ( ), ( ), ( ), ...)p sa p sb p sc . Consider a two player two action game with arbitrary payoffs:
P2
)
Accordingly, we have as required for a mixed strategy Nash equilibrium (James N. Webb, 2006, Game Theory).
1 , 0< p∗ q∗ <
In the real world, company never chooses mixed strategy, since the payoff from mixed strategy is less than both (D, I) and (I, D). Thus, we use the concept of trinomial method to compute the new payoff which higher than the payoff from mixed strategy.
2
Where (j, k) is the payoff from mixed strategy; and are risk-neutral probability. Then, we suppose the following inequality:
1 2
Evaluate these two inequalities, we find the probabilities of three situations as:
)
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