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.

The importance of user-generated photos in restaurant selection

2019 ◽  
Vol 10 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Bruno Oliveira ◽  
Beatriz Casais
Keyword(s):  
Design Methodology ◽  
Online Reviews ◽  
User Generated Content ◽  
Content Type ◽  
Online Platforms ◽  
Photo Sharing ◽  
Restaurant Managers ◽  
Marketing Activities ◽  
Hospitality Sector ◽  
Practical Implications

Purpose User-generated content and online reviews are highly relevant in purchase decision in the hospitality sector, including restaurants, but there is a lack of knowledge about the effect of sharing pictures in this context. This study aims to focus on the relevance of user-generated photos in online platforms for restaurants’ selection. Design/methodology/approach A research was conducted with a sample of 319 residents of Porto region, who had at least one meal in a restaurant over the 30 days before the answer of the survey and had searched online to select the restaurant. Findings The results show that while doing online research about restaurants, it is important for potential consumers to find pictures of food and physical evidences of restaurants generated by other users. Findings also show that consumers find user-generated photos especially at websites of reviews, although the importance of restaurant owned platforms, such as official social media pages and websites. Practical implications The research results appeal restaurant managers to understand the importance of user-generated photos in online platforms by promoting photo sharing in their restaurants with appropriate marketing activities for that purpose. Originality/value This paper expands the state-of-the-art about the importance of user-generated content, focusing on the importance of photos from restaurants shared by consumers in online platforms.

Exploring tourists’ cultural experiences in Naples through online reviews

2017 ◽  
Vol 8 (2) ◽  
pp. 220-238 ◽  
Author(s):  
Maria I. Simeon ◽  
Piera Buonincontri ◽  
Fernando Cinquegrani ◽  
Assunta Martone
Keyword(s):  
Principal Component ◽  
Online Reviews ◽  
Marketing Strategies ◽  
General Level ◽  
Policy Makers ◽  
Content Type ◽  
Cultural Experiences ◽  
Managerial Implications ◽  
Critical Components ◽  
Practical Implications

Purpose This paper aims to analyse online reviews to explore the experiences of tourists related to cultural attractions. Furthermore, the study identifies similarities and differences between cultural attractions and identifies tourists’ preferences. Design/methodology/approach Content analysis and principal component analysis are applied to 12.592 online reviews, in Italian, posted on TripAdvisor by tourists who visited 58 cultural attractions of Naples (Italy) between 2011 and 2014. Findings Findings reveal five critical components of tourists’ experience related to cultural attractions: wonder, authenticity, relaxation, discovery and knowledge. Findings show that tourists can interpret cultural attractions in different ways. Research limitations/implications This study makes advancements on the relationships between tourists’ experience and cultural attractions. Research limitations are related to the geographical context and to the database, which presents a strong standardisation of evaluations, almost never negative. Furthermore, the analysis is limited to online reviews written in Italian language. Future studies will be dedicated to explore reviews in other languages and on other cultural destinations. Practical implications The study draws managerial implications at local and general level. Locally, findings provide suggestions and practical implications to support the tourism policies and marketing of Naples. At general level, the paper provides implications for destination manager organisations and policy makers to strengthen the attractiveness of cultural attractions, develop destination marketing strategies and offer more satisfying cultural experiences. Originality/value This is one of the first studies that uses online reviews to explore the experiences of tourists who visit cultural attractions.

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.

Nature-based experiences in tree houses: guests’ online reviews

2019 ◽  
Vol 74 (3) ◽  
pp. 310-326 ◽  
Author(s):  
Ana Brochado
Keyword(s):  
Design Methodology ◽  
Qualitative Data ◽  
Opportunity To Learn ◽  
Online Reviews ◽  
User Generated Content ◽  
Computer Assisted ◽  
Content Type ◽  
Tourism Studies ◽  
Key Dimensions ◽  
Dominant Narratives

Purpose This study aims to examine nature-based tourists’ experiences in tree houses using user-generated content in Web reviews. The research objectives were to identify the main dimensions of tree house experiences and test whether these dimensions vary according to different traveller-type market segments. Design/methodology/approach A sample of 722 Web reviews was analysed using mixed content analysis methods. Leximancer software provided computer-assisted qualitative data analysis that identified the main themes, after which further qualitative analysis identified the key narratives associated with experiences. Findings The results reveal that tourists are extremely satisfied with their stays in tree houses. The main themes that encompass the dominant narratives are tree house, Costa Rica, staff, morning, walk, wildlife, rainforest, opportunity to learn, trip, experience and recommendation. The narratives vary according to type of traveller. Originality/value The valuable insights gained emphasise the advantages of using user-generated content in tourism studies. The results also offer a better understanding of the key dimensions of this type of nature-based tourism, including a graphic representation of the main themes and concepts in guests’ narratives. In addition, the findings emphasise that nature-based tourists are not a homogeneous group and that they can be segmented according to type of traveller.

Value destruction in exaggerated online reviews

2019 ◽  
Vol 31 (4) ◽  
pp. 1956-1976 ◽  
Author(s):  
Melissa A. Baker ◽  
Kawon Kim
Keyword(s):  
Experimental Design ◽  
Behavioral Intentions ◽  
Negative Emotion ◽  
Online Reviews ◽  
Design Study ◽  
Content Type ◽  
Language Complexity ◽  
Customer Perceptions ◽  
The Impact ◽  
Practical Implications

PurposeThis paper aims to examine the underlying motivations, attitudes and behaviors of exaggerated review posters and readers by examining the effect of review valence, emotional expression and language complexity on perceived poster, website and firm trustworthiness and subsequent behavioral intentions.Design/methodology/approachThis research uses a mixed-method approach using the qualitative critical incident technique (CIT) and quantitative experimental design. Study 1 uses CIT to examine exaggerated online reviews from the poster perspective where Study 2 uses CIT to examine readers’ perceptions of exaggerated reviews. Study 3 conducts a between-subjects experimental design examining the impact of valence (positive vs negative) × emotion (low vs high) × language (vague vs detailed) on trustworthiness and behavior intention.FindingsResults of the two qualitative studies (Study 1 and 2) find posters and readers use language complexity and emotions in exaggerated reviews. The results from the quantitative experimental design study (Study 3) find that language style and emotions influence customer perceptions of poster, website and firm trustworthiness, which also mediates the relationship between the qualitative aspects of review text on behavioral intentions.Practical implicationsThe findings provide multiple practical implications on the prevalence of exaggerated online reviews and the importance of language and emotion in determining customer perceptions and behavioral intentions.Originality/valueBy focusing on both readers and posters in exaggerated eWOM, specific motivations, emotions and language, this research contributes to the literature of online reviews, customer misbehavior, trustworthiness, language use and value co-destruction in online environments.

Integrating Topic, Sentiment, and Syntax for Modeling Online Reviews: A Topic Model Approach

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Min Tang ◽  
Jian Jin ◽  
Ying Liu ◽  
Chunping Li ◽  
Weiwen Zhang
Keyword(s):  
Visual Information ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Online Reviews ◽  
Sentiment Classification ◽  
Academic Field ◽  
Aspect Extraction ◽  
Part Of Speech ◽  
Probabilistic Topic Model ◽  
Topic Sentiment

Analyzing product online reviews has drawn much interest in the academic field. In this research, a new probabilistic topic model, called tag sentiment aspect models (TSA), is proposed on the basis of Latent Dirichlet allocation (LDA), which aims to reveal latent aspects and corresponding sentiment in a review simultaneously. Unlike other topic models which consider words in online reviews only, syntax tags are taken as visual information and, in this research, as a kind of widely used syntax information, part-of-speech (POS) tags are first reckoned. Specifically, POS tags are integrated into three versions of implementation in consideration of the fact that words with different POS tags might be utilized to express consumers' opinions. Also, the proposed TSA is one unsupervised approach and only a small number of positive and negative words are required to confine different priors for training. Finally, two big datasets regarding digital SLR and laptop are utilized to evaluate the performance of the proposed model in terms of sentiment classification and aspect extraction. Comparative experiments show that the new model can not only achieve promising results on sentiment classification but also leverage the performance on aspect extraction.

Investigating the effects of textual reviews from consumers and critics on movie sales

2020 ◽  
Vol 44 (6) ◽  
pp. 1245-1265
Author(s):  
Tianjie Deng
Keyword(s):  
Economic Value ◽  
Online Reviews ◽  
Marketing Strategies ◽  
User Reviews ◽  
Content Type ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Managerial Implications ◽  
Practical Implications ◽  
Unique Dataset

PurposeThe purpose of this paper is to investigate the sales impact of different types of online word-of-mouth based on their source (user vs critic) and form (structured vs unstructured).Design/methodology/approachThe paper proposed a model by adopting the heuristic-systematic perspective of information processing and tested it using online movie reviews collected from Rotten Tomatoes. A unique dataset was constructed, which matched critic reviews and user reviews with metadata such as box-office sales and advertisement spending for 90 movies. Sentiment information from the textual contents of both user and critic reviews were text-mined and extracted. Data analyses were used to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings and critic text reviews.FindingsCritic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives.Practical implicationsThis study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to maximize the economic value of online user-generated information.Originality/valueThe major contribution of this study is to extend the current understanding of the sales impact of online reviews to their textual aspect, as well as investigate how these textual narratives play different roles when offered by critics and users.

Research Topics and Trends in the Maritime Transport: a Structural Topic Model

2020 ◽  
Author(s):  
Xiwen Bai ◽  
Xiunian Zhang ◽  
Kevin X. Li ◽  
Yaoming Zhou ◽  
Kum Fai Yuen
Keyword(s):  
Topic Model ◽  
Maritime Transport ◽  
Research Topics ◽  
Structural Topic Model

scholarly journals Research on comment target extracting in Chinese online shopping platform

2018 ◽  
Vol 2 (3) ◽  
pp. 247-258
Author(s):  
Zhishuo Liu ◽  
Qianhui Shen ◽  
Jingmiao Ma ◽  
Ziqi Dong
Keyword(s):  
Association Rule ◽  
Online Shopping ◽  
Low Frequency ◽  
Online Reviews ◽  
Content Type ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Frequency Feature ◽  
Practical Implications ◽  
Data Reading

Purpose This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value The extracting comment target method the authors proposed in this paper is effective.

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