The paper explores the possibility to employ the source code of corporate websites as an information source for research in innovation studies. Research in this area is generally based on studies that collect data on patents or official data sources. Our paper links the standard economic information of the firm with web-based data and joins the ongoing debate with a threefold contribution. First, whereas the majority of the literature focused on the linguistic content of web-pages, we mostly use HTML tags. Second, we propose a method to assess the quality of the linkage of Web data to firm-level information. Third, we show that the data retrieved from corporate websites can aid to identify 'innovative SMEs'.

Bottai, C., Crosato, L., Domenech, J., Guerzoni, M., Liberati, C. (2022). Unconventional data for policy: Using Big Data for detecting Italian innovative SMEs. In GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social GoodSeptember 2022 (pp.338-344). Association for Computing Machinery [10.1145/3524458.3547246].

Unconventional data for policy: Using Big Data for detecting Italian innovative SMEs

Bottai, Carlo;Guerzoni, Marco;Liberati, Caterina
2022

Abstract

The paper explores the possibility to employ the source code of corporate websites as an information source for research in innovation studies. Research in this area is generally based on studies that collect data on patents or official data sources. Our paper links the standard economic information of the firm with web-based data and joins the ongoing debate with a threefold contribution. First, whereas the majority of the literature focused on the linguistic content of web-pages, we mostly use HTML tags. Second, we propose a method to assess the quality of the linkage of Web data to firm-level information. Third, we show that the data retrieved from corporate websites can aid to identify 'innovative SMEs'.
Si
paper
HTML tags; Innovation; SMEs; Website data;
English
ACM International Conference on Information Technology for Social Good - September 07th-09th, 2022
9781450392846
https://dl.acm.org/doi/proceedings/10.1145/3524458
Bottai, C., Crosato, L., Domenech, J., Guerzoni, M., Liberati, C. (2022). Unconventional data for policy: Using Big Data for detecting Italian innovative SMEs. In GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social GoodSeptember 2022 (pp.338-344). Association for Computing Machinery [10.1145/3524458.3547246].
Bottai, C; Crosato, L; Domenech, J; Guerzoni, M; Liberati, C
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/391635
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