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Information Sciences Letters

Information Sciences Letters

Abstract

Business analytics is a robust strategic management tool, and topic modeling is a technique that can be leveraged to derive insights from vast collections of unstructured data. Topic modeling is an automated method that identifies abstract concepts, or topics," present in various data sources, such as customer feedback, social media posts, and news articles. Through topic modeling, organizations can gain a better understanding of their customers, competitors, and market trends, which can be used to make informed strategic decisions, such as identifying new business opportunities, enhancing product or service offerings, and recognizing potential risks. Moreover, by integrating topic modeling with other business analytics approaches, such as predictive modeling, organizations can gain a more comprehensive perspective of their performance and make data-driven decisions. In essence, topic modeling is a valuable tool for strategic management that provides organizations with the insights they need to stay ahead of the competition and make informed decisions. To make effective strategic decisions, it is crucial to comprehend an organizations internal and external environments fully. The proposed approach utilizes text-mining techniques to augment traditional management tools, such as SWOT analysis or growth-share matrix. By examining narrative materials, such as financial disclosures, we apply topic modeling to identify critical challenges faced by an organization. We then quantify the language used in these materials in terms of risk and optimism, which provides a detailed understanding of a companys strengths and weaknesses and helps identify business units, activities, and processes that may be at risk. Additionally, this approach can be used to compare a company with its competitors or the broader market.

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