Big data analytics and international market selection: An exploratory study
DOI:
https://doi.org/10.37380/jisib.v10i2.581Keywords:
Analytics, big data, export decision-making, international market selectionAbstract
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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