Determinants of Growth in Small Tourism Businesses and the Barriers They Face: The Case of Cappadocia

Abstract The study aims to identify the factors that play a significant role on the growth of small and micro tourism businesses and the barriers of growth. Owners and managers of tourism businesses in Cappadocia, Turkey were surveyed. "Owner/Manager's age", "number of years since the business has a new owner/manager", "creativity and innovation", "frequent provision of trainings and English level of employees" found to be important business growth determinants. Growth barriers and their solutions were also discussed. Overall the study argues that business growth is important for destination and regional development and the topic requires future research

The intellectual structure of the sharing economy

This study analyzes the intellectual structure of the sharing economy (SE) in the hospitality and tourism industry, starting from a sample of 189 papers. A co-citation analysis was performed on the 99 most frequently cited studies. The analysis carried out identified five clusters. These groups include the following: (i) the constituent elements of sharing, (ii) the SE and the sharing phenomenon, (iii) noncommercial website platforms and the social impact generated by sharing firms, (iv) economic impacts, and (v) some negative impacts. Each cluster is succinctly described, presenting the main theme and some subtopics

From Measurement Scale to Sentiment Scale: Examining the Effect of Sensory Experiences on Online Review Rating Behavior

The importance of sensory dimension of customer experiences has been supported in tourism literature; albeit, there are still several important theoretical and methodological gaps to be bridged. This study showcased a methodological procedure of developing a sentiment scale of sensory experiences. By using big online review data in a restaurant context, this study examined the impact of five sensory experiences (sight, smell, sound, taste, and touch) on rating behavior. Results illustrated the relative importance of different sensory experiences. Based on an experiential paradigm, taste experiences were considered the most important component of restaurant experiences. A negativity bias was also observed. This study found a consistent pattern across five sensory experiences that negative attribute had a stronger effect on customer rating than its positive counterpart. The findings are expected to extend the discussions on ‘otherscapes’ and provide theoretical and methodological grounds to develop sentiment scales to understanding tourist experience and behavior

Sharing economy: a co-citation analysis

This study aims to investigate the intellectual structure of P2P accommodation platforms through a co-citation analysis by using a social network approach. To this end, this paper analyzed 79 articles retrieved from the Scopus database. The findings show that there is a clear connection between the intellectual structures of P2P platforms and tourism journals. The cluster analysis identifies four groups, representing the intellectual structure of the P2P platforms. We discuss three relevant topics related to the theoretical pillars: the different levels of analysis, the diverse disciplines involved, and the increasing centrality gain by hospitality and tourism (H&T) studies. For each point, a future research agenda is proposed

Text classification in tourism and hospitality – a deep learning perspective

Purpose: This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research. Design/methodology/approach: This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022. Findings: Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites. Practical implications: The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes. Originality/value: The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.</p

Intellectual Connections in Food Tourism Literature: A Co‐citation Approach

This study critically examines, using a co‐citation approach, the evolution of food and culinary tourism research in the field of hospitality and tourism (H&T) from 1976 to 2019. A bibliometric study of publications indexed in the top 16 H&T journal databases was conducted, and a total of 523 food and culinary tourism‐related documents were identified and analyzed. The research findings revealed that food and culinary tourism publication numbers in H&T journals increased after 1999 yet somehow decreased after 2017. In terms of methodological approaches and data analysis, behavioral studies frequently used structural equation modeling, while advanced methodological approaches in other domains were deemed insufficient. The study findings further reveal that most of the influential works are relatively old and that no groundbreaking or game‐changing studies have occurred in recent years in food and culinary tourism research. Highlights: Analysis of journal categories reveals an increase in the number of articles published in H&T journals after 1999, reaching peak number in 2016. However, food and culinary tourism studies significantly declined in 2017 and attracted even fewer scholars in 2018 and 2019.Most of the influential works are relatively old and that no groundbreaking or game‐changing developments occurred recently in food and culinary tourism research

Competitor Intelligence and Analysis (CIA) Model and Online Reviews: Integrating Big Data Text Mining with Network Analysis for Strategic Analysis

Purpose: This study aims to propose a competitor intelligence and analysis (CIA) model that can be used for the analysis of a firm\u27s competitors. Empirically, it investigates the application of the CIA model on online reviews. This proposed model clarifies the confusion between terms such as competitive intelligence, competitor intelligence and competitor analysis and provides a more efficient process for managers. Design/methodology/approach: The approach of the model integrates text mining techniques as a big data method with network analysis to form a competitor analysis. This study has considered two centrality metrics – degree centrality and betweenness centrality – to identify the functional associations among the resources elaborated by the customers of the hotels. Findings: Findings show online reviews may be used as a solid source of intelligence. The intelligence maps visualized through the text-net technique is an efficient representation of tourist satisfaction and dissatisfaction with a tourism company and its competitors. Practical implications: The proposed approach can be used in the hotel industry along with many others. The implications for scholars and managers and the possible directions for future research are also discussed in the study. Originality/value: This study develops a new approach for competitive intelligence practices in the hotel industry and tests a new method for competitor analysis as a part of the competitive intelligence and analysis approach developed in this study