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The study aims to investigate the adoption of green certification programs by restaurants. More specifically, this study has three objectives: to examine the relationships…
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.
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.
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.
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.
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).
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…
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.
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.
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.
Based on the findings of this study, theoretical and practical implications for hospitality and tourism researchers are provided.
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.
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…
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).
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.
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.
This study demonstrates the application of STM for the systematic analysis of a large amount of text data.
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.
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。
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。
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…
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.
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.
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.
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.
It is the first attempt to apply topic modeling to academic publications and explore the effects of article metadata with the hospitality literature.