Textual data are the last frontier in the empirical literature on proximity between firms. While there are a growing number of studies using textual data, no robust methodology has yet emerged, nor has any attempt been made to compare the resulting findings with standard measures of proximity based on existing classification systems. The purpose of this paper is threefold. First, we propose a methodology that can be an effective and applicable tool for measuring proximity between companies. Second, we compare the resulting indicator of proximity, which we refer to as “business” proximity, with industrial and technological proximity scores based on activity codes and technology adoption, respectively. Third, we use business proximity to explain economic performance, assuming that knowledge sharing can occur between employees working in similar firms. Having established the soundness of the methodology, the empirical results confirm the substantial information content of the descriptive texts and provide evidence on the likelihood of spillover effects between firms that are close in the business and geographical dimension.
So far, yet so close. Using networks of words to measure proximity and spillovers between firms / Marra, Alessandro; Cucculelli, Marco; Cartone, Alfredo. - In: EURASIAN BUSINESS REVIEW. - ISSN 2147-4281. - STAMPA. - (2024). [Epub ahead of print] [10.1007/s40821-024-00270-x]
So far, yet so close. Using networks of words to measure proximity and spillovers between firms
Alessandro Marra;Marco Cucculelli
;
2024-01-01
Abstract
Textual data are the last frontier in the empirical literature on proximity between firms. While there are a growing number of studies using textual data, no robust methodology has yet emerged, nor has any attempt been made to compare the resulting findings with standard measures of proximity based on existing classification systems. The purpose of this paper is threefold. First, we propose a methodology that can be an effective and applicable tool for measuring proximity between companies. Second, we compare the resulting indicator of proximity, which we refer to as “business” proximity, with industrial and technological proximity scores based on activity codes and technology adoption, respectively. Third, we use business proximity to explain economic performance, assuming that knowledge sharing can occur between employees working in similar firms. Having established the soundness of the methodology, the empirical results confirm the substantial information content of the descriptive texts and provide evidence on the likelihood of spillover effects between firms that are close in the business and geographical dimension.File | Dimensione | Formato | |
---|---|---|---|
Eurasian 2024 Marra Cucculelli Cartone.pdf
accesso aperto
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza d'uso:
Creative commons
Dimensione
1.04 MB
Formato
Adobe PDF
|
1.04 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.