A model of optimal international market expansion: The case of US hotel chains expansion into China

Abstract

A comprehensive model of international expansion is outlined and applied to determine the optimal country to be targeted for entry by a US hotel firm and the optimal entry mode to be used. Using a strategic, sequential process, the model performs three levels of assessment: a macro assessment that identifies the major external environmental variables in order to determine the risks and opportunities of international expansion; a micro assessment where environmental variables capturing the countries’ local market conditions and the firm’s specific characteristics are utilized to estimate potential profitability and net present value; and an assessment of the market entry strategy that would be optimal for the target market under consideration. To illustrate how this international expansion assessment model can be used, the three levels of assessment are sequentially applied to a hypothetical US-based hotel company that is representative of major US hotel firms. Though only 12th in the ranking of the top desirable expansion destinations from a macro opportunity/risk perspective, China moves to the top after the micro (country/industry/firm) assessment. For a US-based hotel company, it is determined that expansion into the China mid-market segment through management contracts would provide the optimal value for this firm. By helping managers to quickly identify the optimal country in which to expand from a universe of close to 200 potential targets, the model illustrated here can significantly reduce the time and cost to develop and implement a firm’s international expansion strategy and reduce potential risks of failure and loss. This study highlights the need for a strategic approach to international expansion decisions and the central role that risk assessment and risk management can play in these decisions. It also underscores the importance of country-specific macro-environmental and micro-environmental factors.