Understanding perceptions of professional attributes using conjoint analysis

It is imperative that academia prepare students to meet industry expectations. To that end, this study explores perceptions of professionalism including attributes that are considered to be indicators of future job performance and how they differ among hospitality students, faculty, and industry representatives. Using conjoint analysis on a large data set gathered during a major industry trade show, the results indicate practitioners place tremendous emphasis on interpersonal skills, interview preparation, ability to work with others, and alignment with the organizational culture when deciding whom to hire. Differences among groups are discussed, and resulting inferences on managing related perceptions are addressed.     

Comparing online reviews of hyper-local restaurants using deductive content analysis

This study explored consumer-generated online reviews of six hyper-local restaurants in UK and US. A deductive content analysis was carried out involving intrinsic and extrinsic cues of locally grown food. Overall quality, taste, price, region of origin, and menu information were found to be important variables of hyper-local restaurants’ online reviews. Although there were substantially more reviews about price in the UK-based hyper-local restaurants, consumers were found to be more critical of price in the US restaurants. In the UK hyper-local restaurants, a need to provide great selections and variety in the menu offerings was noticed, whereas the US counterparts showcased a need for more creativity in addition to offering a great selection of food and drinks. Region of origin (local) is more important and positive to consumers in the UK restaurants. Contrary to popular beliefs, health benefit and to some extent freshness were not found to be important variables.     

Segmenting tourists using sustainable values: a cluster analysis of Tanzanian inbound tourism

ABSTRACT

This study aimed at examining different segments of visitors to Tanzania with respect to their values around sustainability. Using a self-administered questionnaire, 286 cases were analyzed through a combination of non-hierarchical and hierarchical cluster analyses. The results indicated three clusters of visitors, each with different sustainable values. A follow-up analysis using ANOVA and Chi-square indicates the three clusters are significantly different in their sustainable values, their travel motives, and their demographics. These results provide insights for both public and private organizations on how to market and manage different destination elements to visitors in a sustainable manner.     

Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews

Business failure prediction or survival analysis can assist corporate organizations in better understanding their performance and improving decision making. Based on aspect-based sentiment analysis (ABSA), this study investigates the effect of customer-generated content (i.e., online reviews) in predicting restaurant survival using datasets for restaurants in two world famous tourism destinations in the United States. ABSA divides the overall review sentiment of each online review into five categories, namely location, tastiness, price, service, and atmosphere. By employing the machine learning–based conditional survival forest model, empirical results show that compared with overall review sentiment, aspect-based sentiment for various factors can improve the prediction performance of restaurant survival. Based on feature importance analysis, this study also highlights the effects of different types of aspect sentiment on restaurant survival prediction to identify which features of online reviews are optimal indicators of restaurant survival.     

Exploring destination's negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on TripAdvisor

Tourism is entering a phase of intellectual changes characterized by consumer power's dynamism through social media. The immense amount of internet knowledge has become the number one means of communication globally through emails and social networks. Any Internet user can freely express themselves and give their views and feelings on any subject, including companies. The current research aims to provide a new technique by combining topic modeling and lexicon-based algorithms to gain insights about Marrakech's e-reputation, enhancing the tourism experience in this city. This study explores about 39,216 TripAdvisor reviews from different locations and attractions in Marrakech, Morocco. This approach is mainly based on Latent Dirichlet Allocation (LDA), to extract hidden aspects and dimensions from the feedbacks of tourists who visited Marrakech, search for some latent patterns inside the reviews and conduct Lexicon-based sentiment analysis to pinpoint the weaknesses directly and develop the touristic experience in this city.