A great deal of information is available on international trade flows and potential markets. Yet many exporters do not know how to identify, with adequate precision, those markets that hold the greatest potential. Even if they have access to relevant information, the sheer volume of information often makes the analytical process complex, time-consuming and costly. An additional challenge is that many exporters lack an appropriate decision-making methodology, which would enable them to adopt a systematic approach to choosing foreign markets. In this regard, big-data analytics can play a valuable role. This paper reports on the first two phases of a study aimed at exploring the impact of big-data analytics on international market selection decisions. The specific big-data analytics system used in the study was the TRADE-DSM (Decision Support Model) which, by screening large quantities of market information obtained from a range of sources identifies optimal product‒market combinations for a country, industry sector or company. Interviews conducted with TRADE-DSM users as well as decision-makers found that big-data analytics (using the TRADE-DSM model) did impact international market-decision. A case study reported on in this paper noted that TRADE-DSM was a very important information source used for making the company’s international market selection decision. Other interviewees reported that TRADE-DSM identified countries (that were eventually selected) that the decision-makers had not previously considered. The degree of acceptance of the TRADE-DSM results appeared to be influenced by TRADE-DSM user factors (for example their relationship with the decision-maker and knowledge of the organization), decision-maker factors (for example their experience and knowledge making international market selection decisions) and organizational factors (for example senior managements’ commitment to big data and analytics). Drawing on the insights gained in the study, we developed a multi-phase, big-data analytics model for international market selection.

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大数据分析和国际市场选择：探索性研究

有关国际贸易流量和潜在市场的大量信息可用。然而，许多出口商不知道如何以足够的精确度来识别那些具有最大潜力的市场。即使他们可以访问相关信息，庞大的信息量也常常使分析过程变得复杂，耗时且昂贵。另一个挑战是，许多出口商缺乏适当的决策方法，这将使他们能够采用系统的方法来选择国外市场。在这方面，大数据分析可以发挥重要作用。本文报告了旨在探索大数据分析对国际市场选择决策的影响的研究的前两个阶段。该研究中使用的特定大数据分析系统是TRADE-DSM（决策支持模型），通过筛选从各种来源获得的大量市场信息，可以确定国家，行业部门或公司的最佳产品‒市场组合。与TRADE-DSM用户以及决策者进行的访谈发现，大数据分析（使用TRADE-DSM模型）确实影响了国际市场决策。本文报道的一个案例研究指出，TRADE-DSM是用于制定公司国际市场选择决策的非常重要的信息来源。其他受访者报告说，TRADE-DSM确定了决策者先前未曾考虑过的国家（最终被选中）。TRADE-DSM结果的接受程度似乎受TRADE-DSM用户因素（例如他们与决策者的关系和组织知识），决策者因素（例如他们的经验和国际知识）的影响。市场选择决策）和组织因素（例如高级管理人员对大数据和分析的承诺）。利用研究中获得的见识，我们为国际市场选择开发了一个多阶段的大数据分析模型。决策者因素（例如，他们的经验和知识制定国际市场选择决策）和组织因素（例如，高级管理人员对大数据和分析的承诺）。利用研究中获得的见识，我们为国际市场选择开发了一个多阶段的大数据分析模型。决策者因素（例如，他们的经验和知识制定国际市场选择决策）和组织因素（例如，高级管理人员对大数据和分析的承诺）。利用研究中获得的见识，我们为国际市场选择开发了一个多阶段的大数据分析模型。