Abstract
Companies within the Digital Economy are evolving their business models as they take advantage of the opportunities afforded by emerging digital technologies. There is a need to develop methods that will allow researchers and policy makers to understand the existence of, and relationships between, the different business models within the Digital Economy and track their evolution. Such methods could also help quantify the size and growth of the Digital Economy. This paper presents a computational method, which utilizes machine learning and web scraping, to identify new business models, and a taxonomy of organisations, through the analysis of a firm's webpage. The work seeks to provide an autonomous tool that provides regular output tracking trends in the number of firms in a market, their business model and changes in activity from product to service over time. This information would provide valuable and actionable insight for researchers, firms and markets.
Publication Date
2022-01-01
Event
55th Hawaii International Conference on System Sciences
Publication Title
Proceedings of the Annual Hawaii International Conference on System Sciences
Volume
2022-January
Publisher
IEEE
ISBN
9780998133157
ISSN
1530-1605
Embargo Period
2024-11-22
First Page
1300
Last Page
1309
Recommended Citation
Wood, Z., Walker, D., & Parry, G. (2022) 'A computational method to track the evolution of business models in the Digital Economy', Proceedings of the Annual Hawaii International Conference on System Sciences, 2022-January, pp. 1300-1309. IEEE: Retrieved from https://pearl.plymouth.ac.uk/secam-research/1670
