Vai ai contenuti. | Spostati sulla navigazione | Spostati sulla ricerca | Vai al menu | Contatti | Accessibilità

| Crea un account

Guseo, Renato - Reinhard , Schuster (2016) Modelling Dynamic Market Potential: Identifying Hidden Automata Networks in the Diffusion of Pharmaceutical Drugs. [Working Paper] WORKING PAPER SERIES, 01/2016 . , PADOVA

Full text disponibile come:

[img]
Anteprima
Documento PDF
4Mb

Abstract (inglese)

This paper proposes an extension of the Guseo–Guidolin model (GGM; Guseo and Guidolin, 2009), with reference to the communication component, which is based on an assumption of complete connectivity of the hidden network supporting the growth of awareness focussed on specific pharmaceutical drugs or products with wide communication investments. The basic ideas are grounded on the extension of the Fibich–Gibori distribution (Fibich and Gibori, 2010) obtained for a minimally connected one dimensional, 1D, network topology and a subsequent inclusion, in a convex combination, with the Bemmaor–Lee distribution (Bemmaor and Lee, 2002), which takes into account unobserved heterogeneity aspects of agents in a market under a complete connectivity.
Based on a continuum between opposite extremes, the extended final model, the network automata NA-GGM, allows the modulation of the involved network in a communication process that determines dynamic market potential applications of GGM are then given for some antidiabetic drugs in Italy. A specific application of the new model is discussed in detail with reference to a statin, Rextat, in the central part of Italy. The proposed extension, NA-GGM, is statistically globally significant with respect to the GGM and is more coherent in commercial behaviour forecasting.


Statistiche Download - Aggiungi a RefWorks
Tipo di EPrint:Working Paper
Anno di Pubblicazione:03 Marzo 2016
Parole chiave (italiano / inglese):Diffusion of innovations models, dynamic market potential, network modulation, pharmaceutical drugs diffusion
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:9605
Depositato il:07 Mar 2016 10:00
Simple Metadata
Full Metadata
EndNote Format

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record