Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls
Ahmed Ghanim Al-Ali, Robert Phaal, Donald Sull
Abstract
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 ana-lyzing earnings calls from Fortune’s 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.
BibTeX
@inproceedings{al-ali-etal-2020-deep,
title = "Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls",
author = "Al-Ali, Ahmed Ghanim and
Phaal, Robert and
Sull, Donald",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.80/",
doi = "10.18653/v1/2020.coling-main.80",
pages = "927--935"
}