Customer Preferences in Small Fast-Food Businesses: A Multilevel Approach to Google Reviews Data
Loading...
No Access Until
Permanent Link(s)
Collections
Other Titles
Authors
Abstract
Online reviews influence customers' decisions and present publicly available data to investigate their preferences on dining experience attributes. This study compares customer reviews of small fast-food businesses to national fast-food chains and builds executable recommendations to small businesses by analyzing 82,598 customer entries from Google Reviews. With text analysis tools and multilevel multinomial models, the study demonstrates that customer reviews for small businesses are less polarized and more positively skewed compared to chain restaurants. The findings also demonstrate the significance of four dining experience attributes: food, service, ambience, and price. The analysis suggests that among these, food and service are the most crucial qualities for fast-food restaurants. While food offerings are essential to get high ratings for small businesses, service is the primary factor in inducing customers to share their feelings. Due to positive skewness in customer ratings, small businesses need to have powerful testimonials to differentiate them from their competitors. Therefore, to build and increase customer base, small fast-food restaurants need to capture the attention of customers with food offerings and promote positive and insightful review contents with service quality.
Journal / Series
Volume & Issue
Description
110 pages
Sponsorship
Date Issued
2020-05
Publisher
Keywords
Customer Preferences; Customer Reviews; Multilevel Model; Restaurants; Small Business; Text Analysis
Location
Effective Date
Expiration Date
Sector
Employer
Union
Union Local
NAICS
Number of Workers
Committee Chair
Just, David
Committee Co-Chair
Committee Member
Hoddinott, John
Liaukonyte, Jurate
Liaukonyte, Jurate
Degree Discipline
Applied Economics and Management
Degree Name
M.S., Applied Economics and Management
Degree Level
Master of Science
Related Version
Related DOI
Related To
Related Part
Based on Related Item
Has Other Format(s)
Part of Related Item
Related To
Related Publication(s)
Link(s) to Related Publication(s)
References
Link(s) to Reference(s)
Previously Published As
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Attribution 4.0 International
Rights URI
Types
dissertation or thesis