Who is a tourist? Classifying international urban tourists using machine learning

A key issue in tourism management relates to the lack of consensus regarding a theoretical and practical definition of the term “tourist.” In turn, this results in a range of methods for counting tourists and measuring tourism. This paper presents a novel non-linear model for classifying international tourists in urban settings, based on machine learning classification methods. These methods utilize innovative feature engineering derived from photos posted on the Flickr social media platform combined with the specific urban destination street structure. The data science model that we developed for identifying international tourists produced an overall accuracy of 69% for Manhattan and 94% for Vienna and Prague, offering new tourism indicators such as repeat visits, travel distances, and short stays. The outcome of this study offers a better understanding of travel patterns among international tourists, which could improve international tourism management and promote a more practical and adaptable model for measuring and analyzing international tourism using machine learning and user-generated content.     

The view from above: the relevance of shared aerial drone videos for destination marketing

ABSTRACT

The use of drones to produce videos has generated a large amount of visually appealing footage of various destinations. They attract much attention, but there are issues that affect their production, and their relevance to destination marketing. This research examines YouTube meta-data and spatial overlay analysis of shared aerial drone videos from the United Kingdom (UK). The results suggest that shared aerial drone videos have some unique user-generated content (UGC) characteristics and their spatial distribution tends to favor more populated areas. Theoretical and practical implications for destination marketing are further discussed.     

Making Reservations Online: The Impact of Consumer-Written and System-Aggregated User-Generated Content (UGC) in Travel Booking Websites on Consumers’ Behavioral Intentions

This study examines how consumer-written and system-aggregated user-generated content (UGC) on travel booking websites jointly influence consumer behavior. The purpose of this research is to test the effects of different types of UGC on various consumer behavior outcomes. Experiment 1 found a significant interaction effect between UGC type (consumer-written versus system-aggregated) and valence (positive versus negative) on product evaluation, perceived information value, and satisfaction. Experiment 2 found a significant interaction between consumer-written and system-aggregated UGC valences. When consumer-written UGC was negative, consumers demonstrated more negative product evaluation and lower buying intention, regardless of system-aggregated UGC valence. When consumer-written and system-aggregated UGC valences matched, consumers indicated greater perceived information value, satisfaction, and future behavioral intention. Trustworthiness partially mediated the effect of the fit between consumer-written and system-aggregated UGC on consumer satisfaction.     

Text mining a decade of progress in hospitality human resource management research: Identifying emerging thematic development

The authors identified the emerging research streams based on the published research literature in human resource management (HRM) from 1994 to 2003 in the International Journal of Hospitality Management. Textual data were collected and content-analyzed by a text-mining program aided by human judgments. The results from the content analysis of both the computer-aided and human judgmental methods were then integrated and conceptually graphed to map meaningful findings that were logically precise, humanly readable, and computationally tractable. Through this unique approach, nine major HRM research themes emerged and each thematic development based on time and country was interpreted and discussed.     

Ecotourism experiences reported online: Classification of satisfaction attributes

This study proposed a quantitative method for evaluating ecotourism experiences reported online by U.S. travelers to Costa Rica. The user-generated content (UGC) used in this study was 373 reviews extracted from TripAdvisor^®. By applying the content analysis technique, 26 attributes that influence ecotourists’ satisfaction with their ecolodge stays were identified and further aggregated into seven categories: ecolodge settings, room, nature, service, food, location, and value for money. A two-step non-parametric statistical procedure was developed to quantitatively support the classification of attributes into satisfiers, dissatisfiers, criticals, and neutrals, the typology first proposed by Cadotte and Turgeon [(1988). Dissatisfiers and satisfiers: suggestions from consumer complaints and compliments. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 1(1), 74-79]. The proposed procedure is considered an original contribution of the article to the literature. The authors hope that the results from this study can be useful to ecolodge managers to evaluate performance in critical areas and develop strategies to maximize customer satisfaction through better utilization of limited resources.     

Not such smart tourism? The concept of e-lienation

The concept of alienation was adapted to tourism by MacCannell who identified it as a key feature of modernity and a strong driver of tourism where tourists seek to reconnect to authentic places and selves. Meanwhile the post-modern world has witnessed a revolution in Information and Communication Technology (ICT) especially in the realm of smart tourism where its advocates talk eagerly of the internet of everything. Such a totalising prospect demands serious review and this article fills a critical gap by conceptualising the idea of e-lienation as a specific form of alienation in ICT-enabled tourism. It combines philosophic questions of meaning, sociological theory and empirical research to demonstrate the meanings of e-lienation, its dimensions, causes, consequences and strategies of resistance.     

Early warning indicators? The effect of consumer and investor sentiments on the restaurant industry

This study looks at the influence of cyclical fluctuations of the consumer confidence index (CCI) and the volatility index (VIX) as early-warning indicators of the variations in restaurant performance. The industry has traditionally focused on past data and on microeconomic influences to anticipate its future performance, a procedure that does not consider possible cyclical fluctuations in restaurant performance metrics. These fluctuations are driven by sentiments of consumers and investors. The study uses the cyclical component of the applied data, followed by unit root and cointegration testing, with subsequent application of the Limited Information Maximum Likelihood technique. The results show both indicators have an effect on restaurant performance, where VIX has an impact on the current, expected, and overall restaurant performance, while the CCI’s influence is only partial (current performance). Policy-makers and planners could benefit from anticipating features of indicators to assess and steer the future performance of the restaurant industry.     

Restaurant experiences triggering positive electronic word-of-mouth (eWOM) motivations

This study empirically examined which restaurant experiences trigger customers to engage in positive electronic word-of-mouth (eWOM), where the quality of restaurant service (food quality, service quality, atmosphere, and price fairness) is the antecedent of eWOM communication. The results of this study suggest that (1) restaurants’ food quality positively influences customers to spread positive eWOM, motivated by their desire to help the restaurant; (2) satisfactory restaurant experiences with service employees triggered positive eWOM, motivated by the need to help the restaurant or to express positive feelings; (3) a superior atmosphere in restaurants elicited positive eWOM motivated by a concern for others; and (4) price fairness in restaurants did not drive restaurant customers toward eWOM. Additionally, this study investigated sources of positive eWOM and types of eWOM media used among opinion leaders in the restaurant industry to enhance the practical implications of the study regarding online marketing. Because of the small number of opinion leaders in the study sample, specifying who the opinion leaders were (the source of eWOM) and the type of eWOM media the opinion leaders used had no effect. Further discussion and implications are provided in the text.