Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls
| Autor: | Robert Phaal, Ahmed Ghanim Al-Ali, Donald N. Sull |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Computation and Language Leverage (finance) Knowledge management Earnings Computer science business.industry 05 social sciences Unstructured data Context (language use) Cloud computing Machine Learning (cs.LG) Computer Science - Computers and Society Digital strategy Leverage (negotiation) Computers and Society (cs.CY) 0502 economics and business 050211 marketing Baseline (configuration management) business Computation and Language (cs.CL) 050203 business & management |
| Zdroj: | COLING |
| DOI: | 10.18653/v1/2020.coling-main.80 |
| Popis: | Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state of the art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by analyzing earnings calls from Fortune Global 500 companies between 2015 and 2019. We use Transformer based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product led, customer experience led, service led, and efficiency led. This work provides an empirical baseline for companies and researchers to enhance our understanding of the field. Comment: Proceedings of The 28th International Conference on Computational Linguistics, 9 pages, 1 figure |
| Databáze: | OpenAIRE |
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