Abstract
Nowadays customers would make decisions by reading online reviews and comparing differences in restaurants before visiting. Therefore how restaurants take advantage from such information is important to attract customers and stay competitive. Many researchers believe that increasing business performance could improve competitiveness. However, with changing customer requirements and business environment, it is challenging to understand which attributes matter the most to customers and how to improve considering competitors. Therefore this study proposed assessing restaurant competitiveness using online reviews. After crawling 38,479 online reviews employing Python, deep learning-based BERT is developed to measure attribute performance and understand the competitiveness through McKinsey Matrix. Then, the competitiveness was analyzed from a temporal dynamic view to present how attributes are changing importance. Notably, the asymmetric effects between attribute performance and satisfaction were considered. Results demonstrated encouraging accuracy in measuring restaurant competitiveness and explained how asymmetric McKinsey Matrix could help formulate efficient competitiveness enhancement strategies.
| Original language | English |
|---|---|
| Article number | 103836 |
| Journal | International Journal of Hospitality Management |
| Volume | 122 |
| Early online date | Jun 2024 |
| DOIs | |
| Publication status | Published - Sept 2024 |
Keywords
- Asymmetric effects
- McKinsey Matrix
- Online reviews
- Restaurant competitiveness
- Sentiment analysis
- Temporal dynamics
ASJC Scopus subject areas
- Tourism, Leisure and Hospitality Management
- Strategy and Management