Capitalizing on big data and revolutionary 5G technology: Extracting and visualizing ratings and reviews of global chain hotels

2022

BFJ

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PurposeResearch on food tourism has a significant impact on destination attractiveness. However, components interfacing food experiences and memory are under-researched topics in food tourism literature. Therefore, this study aims to present a framework based on the components of rememberable food experiences while travelling through the lens of the diffusion of sensory stimulation.Design/methodology/approachThis study adopted a qualitative application of “Memory-Work”, a social constructionist archetype suggested for food tourism-related research. A survey was conducted, and the respondents were asked an open-ended question.FindingsThe analysis found the components instigating these food experiences: Peculiar food and drink experience, setting/geographical location, companions and social interactions, celebrating occasions and touristic components (e.g. serendipitous travel experience and food nostalgic memory). Predominantly, rememberable food tourism experiences are more explicit than memorable tourism experiences.Research limitations/implicationsThe components mentioned in this framework illustrate that various food-related experiences should be involved in destination marketing. Service providers could use these components to create unique destination stories.Originality/valueThis study is the first to present a newly developed framework for food tourism service providers that incorporates sensory impressions with food memories to explore the connection between food memories associated with a destination.

Section: Memorable Tourism Experiences (Mte)mentioning

confidence: 99%

A trip down memory lane to travellers’ food experiences

2022

BFJ

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“…This research aligns with the findings of Dewnarain et al (2019) that CE is a vital success factor for ICS and TR among customers. The pandemic forced individuals to stay home and feared visiting hotels (Gaur et al , 2021b). However, SCRM can develop TR among businesses by paying closer attention to their customers' social behaviour (Chi, 2021).…”

Section: Discussionmentioning

confidence: 99%

Social CRM: linking the dots of customer service and customer loyalty during COVID-19 in the hotel industry

2022

IJCHM

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Purpose Social customer relationship management (SCRM) is an evolving strategy gaining prominence in the hotel industry by cultivating new, improved relationships through engaging customers on social media (SM) platforms. Accordingly, this study aims to assess the effect of SCRM on customer service and customer loyalty (CL) in the hotel industry. This study also explores the moderating effect of COVID-19 (EC) on the relationship between (customer engagement [CE] and improved customer service [ICS]) and (CE and trust [TR]). Design/methodology/approach This study develops and tests the SCRM model using structural equation modelling on a sample size of 214 responses. The questionnaire was administered online to the customers of five preselected global hotel chains. The criteria for selecting the participants were that they must have tweeted from their Twitter handle by using # (hashtag) hotel name to resolve any customer service issues. Findings Results denote that CE significantly impacts ICS. CE was also found to exert a substantial effect on TR. The moderating EC was also found to be significant, but the effect was weak. Although the customers were extensively impacted by the pandemic and were initially hesitant to visit hotels, SCRM proved to be a powerful tool to gain back customer trust (CT) and develop CL by upsurging the shadows of COVID-19. Practical implications This study suggests that viable enforcement of the SCRM system can assist in real-time monitoring and tracking of customers' activities. This can develop a more profound connection with customers through CE which can boost the co-innovation process. Originality/value This study denotes a pioneer attempt to investigate the relationships between SCRM, CE, CT, ICS, CL and COVID-19 in the same framework in a SM context.

“…The sentiments expressed on social media platforms can assist in predicting major and minor fluctuations in businesses. [26] Conducted sentiment analysis on hotel reviews extracted from online review sites and found that the review star ratings on online review sites coincide with the title of online reviews. Deep learning techniques also play a substantial role in sentiment analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

A Latent Dirichlet Allocation Technique for Opinion Mining of Online Reviews of Global Chain Hotels

2022 3rd International Conference on Intelligent Engineering and Management (ICIEM)

2022

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The hospitality industry has faced unprecedented challenges with the outbreak of Covid-19, which has changed customers' expectations. Therefore, it is essential to identify customers' new perceptions and expectations that lead to positive and negative opinions towards the service providers. Accordingly, this study aims to perform topic modeling and sentiment analysis on 94,200 online reviews of five global chain hotels in South Asia. Topic modeling as a text mining, unsupervised machine learning technique can decipher topics from a corpus such as online reviews, online reports, news covers, etc. In this study, the data is extracted from Trip Advisor through web scraping. Topic modeling is performed using the Latent Dirichlet Approach (LDA) on the extracted data set to analyze the key topics mentioned by the customers in the online reviews. The analysis depicted that cleanliness, food, staff, and service were the main concerns of the hotel guests. Furthermore, the findings represented that the main issues impacting the hotel guests were service delays. However, food and services were the keywords with the maximum word count as depicted by topic modeling.