This study is to explore consumer acceptance after implement the system based on "the Unified Theory of Acceptance and Use of Technology" (UTAUT) model. The purpose of our research empirically examined how Venkatesh's UTAUT helped online game companies understand consumer intention to play online game through mobile phones and how this changed as a consumer gained experience of playing online game and web browsing on mobile phones. The consumer's experience of playing online game and web browsing on mobile phones played moderating roles.This research used the questionnaire method to engage in exploration. Then, tested the construct validity by confirmatory factor analysis (CFA), and applied the structural equation modeling (SEM) to examine consumer attitude toward using technology. The findings reveal "Online game experience" and "Web browsing on mobile phones experience" have disturbance effect on consumer intention. The novelty of the study consisted in applying UTAUT to play online game through mobile phones while considering the moderating effects of online game experience and web browsing on mobile phones experience. Significant practical implications can be derived from the results. Finally, management implication and future research are also presented in this research.

This study proposes a forward Monte Carlo method for the pricing of American options. The main advantage of this method is that it does not use backward induction as required by other methods. Instead, the proposed approach relies on a wise determination about whether a simulated stock price has entered the exercise region. The validity of the proposed method is supported by the mathematical proofs for the vanilla cases. With some adaption, it is shown that this forward method can be extended to price other American style options such as chooser and exchange options. This study demonstrates the effectiveness of the proposed approach using a series of numerical examples, revealing significant improvements in numerical efficiency and accuracy in contrast with the standard regressionbased method of Longstaff and Schwartz (2001). C 2012 Wiley Periodicals, Inc. Jrl Fut MarkWe are grateful for the helpful comments and suggestions from Bob Webb (Editor) and an anonymous referee.Due to the lack of analytic solutions to American options prices, researchers have developed a number of methods for these pricing problems. Broadie and Detemple (2004) provided an excellent review summarizing the existing methods for the pricing of American options. Roughly speaking, these approaches can be separated into two main categories: the analytical approximation and the numerical methods. The former is dated back to Barone-Adesi and Whaley's (1987) quadratic approximation, which is regarded as the most representative work of its kind. Many subsequent studies extended their work to consider more complicated options or models, such as the recent examples of Chang, Kang, Kim, and Kim (2007) and Guo, Hung, and So (2009). On the other hand, Monte Carlo simulation is considered to be the most powerful numerical techniques for the valuation of American options. Unlike other methods (e.g., finite difference), the Monte Carlo method has higher flexibility, wider applicability to various products, and is convergent to the true values. Moreover, it is also less sensitive to the problem dimension and therefore better suited to pricing problems involving multiple assets.Using Monte Carlo methods for option pricing dates back to Boyle (1977) for European options. Earlier applications of the Monte Carlo to American option pricing include Tilley's (1993) bundling algorithm, Barraquant and Martineau's (1995) stratified state aggregation algorithm, Broadie and Glasserman's (1997) stochastic tree based algorithm, Longstaff and Schwartz's (2001) regression-based algorithm. Boyle, Broadie, and Glasserman (1997) and Areal, Rodrigues, and Armada (2008) provide a comprehensive review of these methods. The Longstaff and Schwartz's method (also known as the least squares method [LSM]) is perhaps the most popular and promising one of these methods. Many researchers have adopted, modified, and extended this method over the years, including Liu (2010). The main idea of LSM is to estimate the continuation value through a least square regression on future simulated s...