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This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

The purpose of this paper is to explore the key success factors behind Bangtan Boys’ (BTS) popularity, and how they can contribute to sustaining it, along with detailed strategies for the success of global pop.

This study adopts a mixed-methods approach that uses text mining and interviews and uses the success of BTS to find the key factors accounting for its sustained popularity. For use in text mining, we collected data related to BTS from social network sites (SNS) and analyzed this data using latent Dirichlet allocation (LDA) topic modeling, term frequency analysis and keyword extraction. In addition, we conducted interviews to explore the key factors accounting for the sustained popularity of BTS.

We found ten key success factors—active global fandom, SNS communication, fans' loyalty, empathy through music, storytelling and world view, performance quality, music video quality, overseas expansion at an early stage, efforts for self-development and teamwork among members— for a global pop group's success and sustained popularity.

This study contributes to the literature by finding key factors for success and sustained popularity of a global group through using a mixed-methods approach.

Our results suggest strategies to sustain the popularity of global groups and its potential to benefit across the entertainment industry.

This study is among the first to comprehensively examine the key factors for Korean pop’s (K-pop) sustained popularity by using a mixed-methods approach of text mining and interviews.

Building on social media and destination brand-related literature, this study aims to explore World Heritage Sites’ (WHSs) brand diffusion and formation process from long-term and short-term perspectives, which includes brand diffusion, user-generated content (UGC), opinion leaders and brand events’ impact.

This study uses a mixed-method including text mining, keyword analysis and social network analysis to explore the brand formation process of four popular WHSs in Beijing, namely, the Palace Museum, Great Wall, Summer Palace and Temple of Heaven and more than 10,000,000 users’ data on Sina Weibo has been implemented to uncover the underlying social media branding mechanism.

The results show that the number of postings keeps in a stable range in most months, but, in general, there are no common rules for changing trends among the four WHSs; long-term high-frequency keywords related to history and culture account for a higher percentage; different kinds of accounts have varying impacts on information diffusion, in which media accounts lead to a bigger influence. However, more followers do not necessarily mean more interactions and most of the interaction ratio is much lower than 0.01000; brand events facilitate brand dissemination and have an impact on the creation of UGC.

This study is valuable for destination marketers to deeper understand brand diffusion and formation and provides valuable insights for developing effective destination marketing strategies.

Unlike previous studies that only concern a few parts of destination brand formation via social media (e.g. brand diffusion, brand events or opinion leaders’ impact), this study takes a more comprehensive perspective by systematically analyzing the brand formation process of WHSs on social media. By considering both long-term diffusion and short-term representative events, this study provides a more holistic understanding of the branding mechanism.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

Previous research tends to study various sustainable management practices (SMPs) in isolation, leading to inconsistent conclusions about the effect of SMP on firm profit. This research aims to advance the understanding of the linkage between SMP configurations and financial performance by revealing the dynamics of substitutions and complementarities among these SMPs.

The authors conducted an exploratory study based on the theoretical lens of the practice-based view (PBV). An integrated text-mining approach is used in gathering data from 302 corporate reports of cross-sectional firms, followed by a fuzzy-set qualitative comparative analysis method to examine the configurations of SMPs in achieving high and low financial performance.

The findings show that green innovation practices (GIP) and cleaner production practices work together as complements for small firms in configurations associated with high profits. However, large companies in high-competition industries often adopt a minimalist configuration focusing on product quality and safety management (PQSM). PQSM and GIP are complements in configuration for small firms in high-competition industries, while they work together as substitutes for small firms in low-competition industries.

This research provides managers with a new way of thinking to support decision-making in sustainable operations management. The complementary and substitutive view of management practice can guide operations managers to design and implement effective practice configurations to achieve synergistic effects.

This is among the first studies to reveal the complex relationship between various configurations of SMPs and firm performance. In addition, this research offers new insights into previously inconsistent findings from a PBV and configuration perspective.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

This study aims to develop a holistic and dynamic model that governs the various relationships among the critical factors of crisis management from a stakeholder perspective in the context of China’s COVID-19 epidemic outbreak.

Data were collected from 731 textual sources, and the text mining technique identified the themes of a holistic crisis management model. Then, content analysis was applied to reveal in-depth insights into the themes.

From a stakeholder perspective, the model comprises six elements: political environment, economic environment, technology, social environment, health and science and international relationships, which relate significantly to four procedural actions: prevention, response, recovery and adaptation. The overlapping stages and situational dynamic mechanisms of the process are another two new major findings of this study; learning and preparing are threaded throughout the whole dynamic process.

Hospitality stakeholders are advised to collaborate under the guidance of the dynamic crisis management model and adopt high-technology tools for the industry’s recovery management. Developing a new business model and marketing strategy is a useful approach to face similar crisis management challenges in the future.

This paper fills an existing research gap by presenting a health-related crisis management model that can be used to evaluate the dynamic process of collaborations among stakeholders in coping with external challenges forced upon the hospitality industry.

利益相关者视角下的危机管理动态模型：以covid-19在中国的发展为例

本研究以中国新冠肺炎疫情为背景, 从利益相关者的角度构建危机管理关键因素之间各种关系的整体动态模型。

本研究从731个文本来源收集定量数据, 并通过文本挖掘技术识别危机管理模型的构成要素,并运用内容分析对构成要素进行深入剖析。

从利益相关者的角度来看, 该模型包括六个要素:政治环境、经济环境、技术、社会环境, 健康科学和国际关系。这些要素与预防、反馈、恢复和适应这四项运行程序密切相关。本研究的两大发现是防疫机制的动态机制和重叠特性, 学习和预备贯穿于疫情防控的整个动态过程。

酒店利益相关者应在动态危机管理模型的指导下进行合作, 并运用高科技进行行业复苏管理。此外, 开发新的商业模式和营销策略也是面对新的外部环境的有效途径。

本文通过提出一个健康相关的危机管理模型来填补现有研究的空白, 该模型可用于评估酒店行业外部环境中利益相关者之间的动态合作过程。

En este estudio se recogieron datos cuantitativos de 731 fuentes textuales, se identificaron los elementos constitutivos del modelo de gestión de crisis a través de la minería textual y se analizaron en profundidad los elementos constitutivos mediante el análisis de contenido.

este estudio utiliza una combinación de minería de texto y análisis de contenido para explorar el manejo de crisis del brote de covid-19 en el sector turístico y hotelero de China. En primer lugar, se recogieron datos cuantitativos de 731 fuentes textuales y se identificaron los elementos constitutivos del modelo de gestión de crisis a través de la minería textual. En segundo lugar, se utiliza el análisis de contenido para analizar en profundidad los elementos constitutivos y, sobre la base de UN análisis de datos exhaustivo y una revisión de la literatura, se llega a UN marco teórico para el manejo de crisis.

Desde el punto de vista de las partes interesadas, el modelo consta de seis elementos: el entorno político, el entorno económico, el entorno tecnológico, el entorno social, la ciencia de la salud y las relaciones internacionales. Estos elementos están estrechamente relacionados con los cuatro procedimientos operativos: prevención, retroalimentación, recuperación y adaptación. Los dos principales hallazgos de este estudio son los mecanismos dinámicos y las características de superposición de los mecanismos de prevención de epidemias, y el aprendizaje y la preparación a lo largo de todo el proceso dinámico de prevención y control de brotes.

las partes interesadas del sector hotelero deben colaborar bajo la guía de UN modelo dinámico de gestión de crisis y utilizar la alta tecnología para la gestión de la recuperación del sector. Además, el desarrollo de nuevos modelos de negocio y estrategias de marketing es también una forma eficaz de afrontar el nuevo entorno externo.

este artículo colma las lagunas de la investigación existente proponiendo UN modelo de gestión de crisis relacionadas con la salud que pueda ser utilizado para evaluar procesos dinámicos de colaboración entre los interesados en el entorno externo del sector hotelero.

This study aims to establish a comprehensive satisfaction analysis framework by mining online restaurant reviews, which can not only accurately reveal consumer satisfaction but also identify factors leading to dissatisfaction and further quantify improvement opportunity levels.

Adopting deep learning, Cross-Bidirectional Encoder Representations Transformers (BERT) model is developed to measure customer satisfaction. Furthermore, opinion mining technique is used to extract consumers’ opinions and obtain dissatisfaction factors. Furthermore, the opportunity algorithm is introduced to quantify attributes’ improvement opportunity levels. A total of 19,133 online reviews of 31 restaurants in Universal Beijing Resort are crawled to validate the framework.

Results demonstrate the superiority of Cross-BERT model compared to existing models such as sentiment lexicon-based model and Naïve Bayes. More importantly, after effectively unveiling customer dissatisfaction factors (e.g. long queuing time and taste salty), “Dish taste,” “Waiters’ attitude” and “Decoration” are identified as the three secondary attributes with the greatest improvement opportunities.

The proposed framework helps managers, especially in the restaurant industry, accurately understand customer satisfaction and reasons behind dissatisfaction, thereby generating efficient countermeasures. Especially, the improvement opportunity levels also benefit practitioners in efficiently allocating limited business resources.

This work contributes to hospitality and tourism literature by developing a comprehensive customer satisfaction analysis framework in the big data era. Moreover, to the best of the authors’ knowledge, this work is among the first to introduce opportunity algorithm to quantify service improvement benefits. The proposed Cross-BERT model also advances the methodological literature on measuring customer satisfaction.

The main goal of this paper is to identify the attributes of consumer experience in Michelin-starred restaurants and to estimate their effects on restaurant ratings.

A sample of 70,233 online reviews of 224 Spanish Michelin-starred restaurants were analysed with the latent Dirichlet allocation algorithm. A sentiment analysis and a logistic regression analysis were also employed to estimate the effect of attributes on restaurant ratings.

Customer attention, food quality, decor and ambience and value for money are frequently used to define restaurant experience. However, it is shown in this study that the experience in a Michelin-starred restaurant goes beyond the evaluation of those four attributes. Furthermore, the effect of the factors that were identified on customer satisfaction differed depending on the restaurant ratings.

The findings are linked to the context of Spanish Michelin-starred restaurants. It is also assumed in this study that online reviews are based on truthful opinions.

Restaurant managers should primarily focus on customer attention and food quality to achieve customer satisfaction. In addition, those restaurants with an error-free service and a highly appreciated wine list among diners are more likely to achieve the culinary excellence that deserves a 5-star rating on TripAdvisor.

The attributes of the restaurant experience are frequently identified in literature reviews. Research based on text-mining analyses of customer reviews to discover a posteriori the factors that define a restaurant experience is scarce, and particularly difficult to find in the context of Michelin-starred restaurants.

This study aims to identify the impact of restaurant pandemic mitigation measures on perceived service quality.

A total of 15,251 reviews were collected from 300 TripAdvisor Lisbon restaurant Web pages between March 2020 and December 2021, when Covid-19 contingency measures were in place. Grounded on the online reviews, a word frequency matrix was created and used as input on partial least squares–structural equation modeling to test the proposed hypotheses.

The results suggest that precaution measures, such as the Vaccination Certificate, negative tests and restaurant layout configuration, positively influenced perceived service quality and consumer satisfaction, moderated by consumers’ beliefs.

This study provides relevant information for restaurant managers, which will help them implement strategies to guarantee service quality, consumer satisfaction and revisit intentions in future pandemic scenarios.