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Article
Publication date: 24 July 2020

Eunhye (Olivia) Park, Woo-Hyuk Kim and Junehee Kwon

The study aims to investigate the adoption of green certification programs by restaurants. More specifically, this study has three objectives: to examine the relationships…

Abstract

Purpose

The study aims to investigate the adoption of green certification programs by restaurants. More specifically, this study has three objectives: to examine the relationships between green certification program scores and customers’ perceptions, duration of green certification and green brand image and food-focused green practices and green brand image.

Design/methodology/approach

The authors collected 25,098 TripAdvisor reviews, along with associated patron demographics, for 70 green certified restaurants. To investigate the hypotheses, the authors first used structural topic modeling to discover latent themes relevant to green restaurant practices. Thereafter, the authors used factorial Multivariate analysis of covariance (MANCOVA) to examine the association between formal certification participation and customers’ green perceptions.

Findings

The results showed that customers were more likely to perceive a green restaurant image after visiting green certified restaurants with higher certification ratings and green certification periods of longer duration.

Practical implications

The current study contributes to the literature in several ways. First, this study uses post-visit online reviews written by customers of certified green restaurants to understand customers’ natural responses more precisely. Second, the study captures the degree of green commitment by applying information about formal certification programs, where other studies have relied on hypothetical scenarios or survey questions to examine the impact of green attributes on customer perceptions.

Originality/value

To the best of authors’ knowledge, this is the first study to investigate the adoption of green certification programs by restaurants empirically with data drawn from actual user-generated content (i.e. TripAdvisor).

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 3 June 2019

Faizan Ali, Eunhye (Olivia) Park, Junehee Kwon and Bongsug (Kevin) Chae

This paper aims to showcase the trends in the research topics and their contributors over a time period of 30 years in the International Journal of Contemporary

Abstract

Purpose

This paper aims to showcase the trends in the research topics and their contributors over a time period of 30 years in the International Journal of Contemporary Hospitality Management (IJCHM). To be specific, this paper uncovers IJCHM’s latent topics and hidden patterns in published research and highlights the differences across three decades and before and after Social Sciences Citation indexing.

Design/methodology/approach

In total, 1,573 documents published over 199 issues of IJCHM were analyzed using two computational tools, i.e. metaknowledge and structural topic modeling (STM), as the basis of the mixed method. STM was used to discover the evolution of topics over time. Moreover, bibliometrics (and network analysis) were used to highlight IJCHM’s top researchers, top-cited references, the geographical networks of the researchers and differences in the collaborative networks.

Findings

The number of papers published continually increased over time with changes of key researchers publishing in IJCHM. The co-authorship networks have also changed and revealed an increasing diversity of authorship and collaborations among authors in different countries. Moreover, the variety of topics and the relative weight of each topic have also changed.

Research limitations/implications

Based on the findings of this study, theoretical and practical implications for hospitality and tourism researchers are provided.

Originality/value

It is the first attempt to apply topic modeling to a leading academic journal in hospitality and tourism and explore the diversity in contemporary hospitality management research (topics and contributors) from 30 years of published research.

Details

International Journal of Contemporary Hospitality Management, vol. 31 no. 7
Type: Research Article
ISSN: 0959-6119

Keywords

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Article
Publication date: 24 August 2018

Eunhye (Olivia) Park, Bongsug (Kevin) Chae and Junehee Kwon

The purpose of this study was to explore influences of review-related information on topical proportions and the pattern of word appearances in each topic (topical…

Abstract

Purpose

The purpose of this study was to explore influences of review-related information on topical proportions and the pattern of word appearances in each topic (topical content) using structural topic model (STM).

Design/methodology/approach

For 173,607 Yelp.com reviews written in 2005-2016, STM-based topic modeling was applied with inclusion of covariates in addition to traditional statistical analyses.

Findings

Differences in topic prevalence and topical contents were found between certified green and non-certified restaurants. Customers’ recognition in sustainable food topics were changed over time.

Research limitations/implications

This study demonstrates the application of STM for the systematic analysis of a large amount of text data.

Originality/value

Limited study in the hospitality literature examined the influence of review-level metadata on topic and term estimation. Through topic modeling, customers’ natural responses toward green practices were identified.

研究目的

本研究旨在通过结构性话题建模(STM)方法以开拓评论性内容对于话题组成和词条构成的影响。

研究设计/方法/途径

本论文采用 173,607 份 Yelp.com 在 2015 至 2016 年间的评论内容为样本,STM 分析结合共变量形成话题性建模。

研究结果

话题趋势和话题内容的不同存在于认证过的绿色餐馆与非认证的绿色餐馆中。消费者对于可持续性的食物话题兴趣随着时间而改变。

研究理论限制/意义

本研究对 STM 相关大规模文本型数据的系统分析方法给与启示。

研究原创性/价值

在酒店管理文献中很少有文章研究评论性元数据对于话题和词条预估的影响。通过话题建模,消费者对于绿色措施的反馈获得了梳理和确认。

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Article
Publication date: 10 August 2018

Eunhye (Olivia) Park, Bongsug Chae and Junehee Kwon

This paper aims to identify the intellectual structure of four leading hospitality journals over 40 years by applying mixed-method approach, using both machine learning…

Abstract

Purpose

This paper aims to identify the intellectual structure of four leading hospitality journals over 40 years by applying mixed-method approach, using both machine learning and traditional statistical analyses.

Design/methodology/approach

Abstracts from all 4,139 articles published in four top hospitality journals were analyzed using the structured topic modeling and inferential statistics. Topic correlation and community detection were applied to identify strengths of correlations and sub-groups of topics. Trend visualization and regression analysis were used to quantify the effects of the metadata (i.e. year of publication and journal) on topic proportions.

Findings

The authors found 50 topics and eight subgroups in the hospitality journals. Different evolutionary patterns in topic popularity were demonstrated, thereby providing the insights for popular research topics over time. The significant differences in topical proportions were found across the four leading hospitality journals, suggesting different foci in research topics in each journal.

Research limitations/implications

Combining machine learning techniques with traditional statistics demonstrated potential for discovering valuable insights from big text data in hospitality and tourism research contexts. The findings of this study may serve as a guide to understand the trends in the research field as well as the progress of specific areas or subfields.

Originality/value

It is the first attempt to apply topic modeling to academic publications and explore the effects of article metadata with the hospitality literature.

Details

International Journal of Contemporary Hospitality Management, vol. 30 no. 11
Type: Research Article
ISSN: 0959-6119

Keywords

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