Is AI really better than conventional methods to identify the key drivers of firms’ performance? An exploratory study in the hospitality industry
Bibliographic Data
| ID | 21698308 |
|---|---|
| Authors | Francesca D’angella (0000-0001-5110-1504, Università IULM, corresponding author), Guido Ferilli (0000-0002-1086-035X, IULM University), Manuela De Carlo (0000-0003-2490-5333, IULM University), Massimo Buscema (0000-0003-4356-0510, University of Colorado System), P M Buscema (Semeion Research Center) |
| Year | 2025 |
| Volume | 28 |
| Issue | 20 |
| Pages | 3223-3230 |
| Publication date | 2025-10-18 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Current Issues in Tourism (JOURNAL) |
| Journal identifiers | ISSN: 1368-3500 • E-ISSN: 1747-7603 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13683500.2024.2392751 |
| OpenAlex | W4402344809 |
| Language | EN |
| Citations received | 1 |
| References cited | 21 |
ANNs are a key theme in tourism and hospitality. However, they haven’t been widely employed to identify internal and external determinants of firms’ performance. Based on a dataset of daily hotels’ reservations, we demonstrate the capability of a specific unsupervised ANN (Auto Contractive Map – CM) to outperform other methods in assessing the hidden connections among variables and providing guidelines for hotel managers to improve competitive performance. Since AutoCM results are static, they could be enriched by dynamic simulation tools to measure the impact of the variation of key determinants of company’s performance on the entire data ecosystem
Business · Computer security · Exploratory research · Hospitality · Hospitality industry · Industrial organization · Process management · Tourism · Big Data and Business Intelligence · Computer Science · Forecasting Techniques and Applications · Stock Market Forecasting Methods · Marketing
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |