scholarly journals The Effects of Green Restaurant Attributes on Customer Satisfaction Using the Structural Topic Model on Online Customer Reviews

2020 ◽  
Vol 12 (7) ◽  
pp. 2843 ◽  
Author(s):  
Eunhye (Olivia) Park ◽  
Bongsug (Kevin) Chae ◽  
Junehee Kwon ◽  
Woo-Hyuk Kim
Keyword(s):  
Customer Satisfaction ◽  
Topic Model ◽  
Marketing Strategies ◽  
Restaurant Industry ◽  
User Generated Content ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Longitudinal Approach ◽  
The Common ◽  
Structural Topic Modeling

Although green practice is increasingly adopted in the restaurant industry, there is still little research in terms of investigating the impacts of green practice on customer satisfaction. This study utilized user-generated content by green restaurant customers to identify various aspects of green restaurants, including perceived green restaurant practices. Our data are based on U.S. green-certified restaurants available on Yelp. Structural topic modeling was used to discover latent restaurant attributes from user-generated content. With a longitudinal approach, the changes in customers’ interest in green practices were estimated. Finally, the common restaurant attributes and green attributes were used to predict customer satisfaction. This study will contribute to marketing strategies for the restaurant industry.

scholarly journals Analysis Of Online Customer Reviews

2011 ◽  
Vol 2 (10) ◽  
Author(s):  
James R. Otto ◽  
William Wagner
Keyword(s):  
Customer Satisfaction ◽  
Service Delivery ◽  
Customer Service ◽  
Purchase Price ◽  
Font Size ◽  
Online Retail ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Online Retailers ◽  
Retail Managers

<p style="text-align: justify; margin: 0in 0.5in 0pt;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-bidi-font-style: italic; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 'Times New Roman';">The overall satisfaction of the customer is an important issue for online retailers.<span style="mso-spacerun: yes;">&nbsp; </span>This paper analyzes online customer ratings of electronic goods in the areas of </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-bidi-font-style: italic; mso-bidi-font-size: 12.0pt;">Overall Customer Satisfaction, Customer Service, Delivery, Ease of Purchase, Price, and Shipping Options.<span style="mso-spacerun: yes;">&nbsp; </span>The authors develop neural network and multiple regression models that relate Overall Customer Satisfaction evaluations to the other rating factors.<span style="mso-spacerun: yes;">&nbsp; </span>By using these models, online retail managers can determine how to best allocate their resources to improve customer service, delivery, ease of purchase, price, and/or shipping options in ways that can best improve overall customer satisfaction.</span></p>

What Are the Salient and Memorable Green-Restaurant Attributes? Capturing Customer Perceptions From User-Generated Content

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110315
Author(s):  
Eunhye Park ◽  
Junehee Kwon ◽  
Bongsug (Kevin) Chae ◽  
Sung-Bum Kim
Keyword(s):  
Qualitative Data ◽  
Topic Model ◽  
Online Reviews ◽  
Marketing Strategies ◽  
User Generated Content ◽  
Customer Perceptions ◽  
Modeling Methodology ◽  
Probabilistic Topic Model ◽  
Practical Implications ◽  
Structural Topic Model

This study aims to survey user-generated content (UGC) from diners in certified green restaurants, discover the green images they recall, and demonstrate the usefulness of applying a probabilistic topic model to comprehend customers’ perceptions. Postvisit online reviews ( N = 28,098), in the form of unstructured texts from the TripAdvisor.com website, were used to find freely recalled green-restaurant images. These data were preprocessed with a structural topic model (STM) algorithm to select 51 relevant categories of images. These image categories were compared with the findings of previous studies to discover unique restaurant attributes. Furthermore, a topic-level network and a green-restaurant network were drawn to discover the most easily recallable image categories and their attributes. This machine-learning-based approach improved the reproducibility of unstructured data analyses, overcoming the subjectivity of qualitative data analysis. Theoretical and practical implications are offered for topic modeling methodology along with marketing strategies for restaurateurs.

scholarly journals Customer Experience and Satisfaction of Disneyland Hotel through Big Data Analysis of Online Customer Reviews

2021 ◽  
Vol 13 (22) ◽  
pp. 12699
Author(s):  
Xiaobin Zhang ◽  
Hak-Seon Kim
Keyword(s):  
Factor Analysis ◽  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Linear Regression Analysis ◽  
Customer Experience ◽  
Future Research ◽  
Significant Information ◽  
Customer Reviews ◽  
Online Customer Reviews

Online customer reviews have become a significant information source for scholars and practitioners to understand customer experience and its association with their satisfaction to maintain the sustainable development of relative industries. Thus, this study attempted to find the underlying dimensionality in online customer reviews reflecting customers experience in the Hong Kong Disneyland hotel and identified its relationship with customer satisfaction. Semantic network analysis by Netdraw and factor analysis and linear regression analysis by SPSS 26.0 (IBM, New York, NY, USA) were applied for data analysis. As a result, 70 keywords with high frequency were extracted, and their connection to each other was calculated based on their centralities. Consequently, seven factors were explored by exploratory factor analysis, and moreover, three factors, “Family Empathy”, “Value”, and “Food Quality”, were testified to be negatively related to customer satisfaction. The findings of this study, to a great extent, could be utilized as a research scheme for future research to investigate theme hotels with big data analytics of online customer reviews. More importantly, some new insights and practical implications for the future research and industry development were provided and discussed as well.

scholarly journals Auto Defect Detection Using Customer Reviews for Product Recall Insurance Analysis

2021 ◽  
Vol 7 ◽  
Author(s):  
Titus Hei Yeung Fong ◽  
Shahryar Sarkani ◽  
John Fossaceca
Keyword(s):  
Risk Analysis ◽  
Defect Detection ◽  
Product Life Cycle ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Product Recall ◽  
Customer Reviews ◽  
Novel Approach ◽  
Online Customer Reviews ◽  
Product Risk

The challenge for Product Recall Insurance companies and their policyholders to manually explore their customer product’s defects from online customer reviews (OCR) delays product risk analysis and product recall recovery processes. In today's product life cycle, product recall events happen almost every day and there is no practical method to automatically transfer the massive amount of valuable online customer reviews, such as defect information, performance issue, and serviceability feedback, to the Product Recall Insurance team as well as their policyholders’ engineers to analyze the product risk and evaluate their premium. This lack of early risk analysis and defect detection mechanism often increases the risks of a product recall and cost of claims for both the insurers and policyholder, potentially causing billions of dollars in economic loss, liability resulting from the bodily injury, and loss of company credibility. This research explores two different kinds of Recurrent Neural Network (RNN) models and one Latent Dirichlet Allocation (LDA) topic model to extract product defect information from OCRs. This research also proposes a novel approach, combined with RNN and LDA models, to provide the insurers and the policyholders with an early view of product defects. The proposed approach first employs the RNN models for sentiment analysis on customer reviews to identify negative reviews and reviews that mention product defects, then applies the LDA model to retrieve a summary of key defect insight words from these reviews. Results of this research show that both the insurers and the policyholders can discover early signs of potential defects and opportunities for improvement when using this novel approach on eight of the bestselling Amazon home furnishing products. This combined approach can locate the keywords of these products’ defects and issues that customers mentioned the most in their OCRs, which allows the insurers and the policyholders to take required mitigation actions earlier, proactively stop the diffusion of the detective products, and hence lower the cost of claim and premium.

scholarly journals Online Customer Reviews on Restaurant Using Blockchain

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 269-277
Author(s):  
D. Saveetha ◽  
Dr.G. Maragatham
Keyword(s):  
Rental Housing ◽  
Digital Technologies ◽  
Restaurant Industry ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Guest House ◽  
Huge Impact ◽  
Fake Reviews ◽  
Financial Losses ◽  
Business Trip

Modern day businesses are largely dependent on digital technologies. People prefer viewing the reviews before making any decisions. It applies to all consumables like buying Electronic items, Clothing, Travel, Guest-House, Restaurant, Rental, Housing, Automobile, Cosmetics, Jewellery, Movies, etc. Online services like Mantra, Yelp, Amazon, Facebook, Google My Business, Trip Advisor offer great services to the customer. However, drawbacks of these systems are fake reviews, negative reviews and sometimes even tampering of the reviews given by the customers, which has a huge impact on the business leading to huge financial losses. Sometimes a competitor in the business might also influence the ratings being provided. The centralized storage of these reviews also leads to problems like tampering or manipulation of the data being stored. In this paper we propose an application in the restaurant industry that solves all these drawbacks by making use of the Ethereum blockchain. The food reviews given by the customers are stored as smart contracts in the blockchain, which can't be altered, thus guaranteeing the authenticity of the reviews. Validity of the reviews is ensured because it is difficult for the restaurants to delete or create new accounts to wipe away the bad reviews given. Blockchain is immutable so we ensure that the reviews are genuine and the system is trustable.

scholarly journals ARTI PENTING TINGKAT KEPUASAN PARA PELANGGAN DAN PEMANFAATAN ULASAN DARING

2018 ◽  
Author(s):  
Dewanta Fachrureza
Keyword(s):  
Customer Satisfaction ◽  
Online Reviews ◽  
Online Review ◽  
Hotel Management ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Front Office

<p>ABSTRACT</p><p>This research departs from the curiosity of researchers to find out the extent to which online customer reviews are used at the Ritz Carlton hotel, because hotel management responds well even to the extraordinary in responding to online customer reviews, especially from TripAdvisor. The purpose of this study is to develop and understand the extent to which online review customer reviews are used from TripAdvisor to the department of the front office at the Ritz Carlton Hotel Jakarta. Conclusions from this study are important for hotels to maintain and improve the level of customer satisfaction to improve the quality of hotel services. The researcher also gave several suggestions which stated that there must be a position of work that is responsible for ensuring that all online reviews will be answered and evaluated. In addition, the hotel must invite more guests to comment on TripAdvisor.<br />Keywords: Customer, Customer Satisfaction, Online Review, Front Office Department</p>

Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews

2020 ◽  
Vol 83 ◽  
pp. 101760 ◽  
Author(s):  
Filipe R. Lucini ◽  
Leandro M. Tonetto ◽  
Flavio S. Fogliatto ◽  
Michel J. Anzanello
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Customer Reviews ◽  
Online Customer Reviews

scholarly journals An Approach to Discovering Product/Service Improvement Priorities: Using Dynamic Importance-Performance Analysis

2018 ◽  
Vol 10 (10) ◽  
pp. 3564 ◽  
Author(s):  
Jiacong Wu ◽  
Yu Wang ◽  
Ru Zhang ◽  
Jing Cai
Keyword(s):  
Performance Analysis ◽  
Customer Satisfaction ◽  
Market Competition ◽  
Service Improvement ◽  
Target Product ◽  
Customer Reviews ◽  
Review Mining ◽  
Feature Group ◽  
Online Customer Reviews ◽  
Product Service

The cost budget and resources of a business are limited. In order to be competitive sustainably in the market, it is necessary for a businesses to discover the improvement priorities of their product/service features effectively and allocate their resources appropriately for higher customer satisfaction. Online customer review mining has been attracting increasing attention for businesses to discover priorities of product/service improvement from online customer reviews. Despite some prior related studies, their methods have several limitations, such as simply using the frequencies of mentioned product features in reviews as an indicator of importance; neglecting the market competition; and focusing only on the static importance and performance of the target product/service features. To address those limitations, this study proposes a novel approach to discovering a product/service’s improvement priorities through dynamic importance-performance analysis of online customer reviews. It first clusters similar features into a feature group and calculate the relative performance of the feature groups using sentiment analysis. Next, the importance of each feature group’s performance to overall customer satisfaction is measured by the factor categories based on the Kano’s model. The factor categories are determined by the significance values of each feature group in both positive and negative sentiment polarities derived from the constructed decision tree. Finally, feature improvement priorities of a target product/service will be discovered based on the dynamic performance trend and predicted importance using a dynamic importance-performance analysis. The evaluation results show that the dynamic importance-performance analysis approach proposed in this study is a much better approach for product/service improvement priorities discovering than the product opportunity mining approach proposed in the prior studies. This study makes new research contributions to automatic discovery of product/service improvement priorities from large-scale online customer reviews. The proposed approach can also be used for product/service performance monitoring and customer needs analysis to improve product/service design and marketing campaigns.

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