Forecasting the Business Performance of Restaurants on Social Commerce

• 영문명: Forecasting the Business Performance of Restaurants on Social Commerce
• 발행기관: 한국유통과학회
• 저자명: Supamit BOONTA Kanjana HINTHAW
• 간행물 정보: 『유통과학연구(JDS)』제22권 제4호, 11~22쪽, 전체 12쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2024.04.30

영문 초록

Purpose: This research delves into the various factors that influence the performance of restaurant businesses on social commerce platforms in Bangkok, Thailand. The study considers both internal and external factors, including but not limited to business characteristics and location. Moreover, this research also analyzes the effects of employing multiple social commerce platforms on business efficiency and explores the underlying reasons for such effects. Research design, data, and methodology: Restaurants can be classified into different price ranges: low, medium, and high. To further investigate, we employed natural language processing AI to analyze online reviews and evaluate algorithm performance using machine learning techniques. We aimed to develop a model to gauge customer satisfaction with restaurants across different price categories effectively. Results: According to the research findings, several factors significantly impact restaurant groups in the low and mid-price ranges. Among these factors are population density and the number of seats at the restaurant. On the other hand, in the mid-and high-price ranges, the price levels of the food and drinks offered by the restaurant play a crucial role in determining customer satisfaction. Furthermore, the correlation between different social commerce platforms can significantly affect the business performance of high-price range restaurant groups. Finally, the level of online review sentiment has been found to influence customer decision-making across all restaurant types significantly. Conclusions: The study emphasizes that restaurants’ characteristics based on their price level differ significantly, and social commerce platforms have the potential to affect one another. It is worth noting that the sentiment expressed in online reviews has a more significant impact on customer decision-making than any other factor, regardless of the type of restaurant in question.

목차

1. Introduction

2. Literature Review

3. Research Methodology

4. Results and Discussion

5. Conclusions and Future Work

Acknowledgment

References

키워드

Social Commerce Machine Learning Sentiment Analysis Business Performance Prediction Distribution Science