Destination Image Analysis Research Articles

Destination image analysis and marketing strategies in emerging panda tourism: a cross-cultural perspective

Destination image analysis and marketing strategies in emerging panda tourism: a cross-cultural perspective

Assessing the Tourist Appeal of a North African Destination: An Analysis of Destination Image and its Effect on Recommendations and Revisit Intentions

ABSTRACT Algeria is actively enhancing its destination image for sustainable tourism through the “Tourism Development Plan: Horizons 2030,” aiming to boost its allure as a tourist destination. However, recent literature lacks an exploration of specific attributes’ impact on visitors’ behavior. A recent study on 338 Libyan visitors, utilizing Structural Equation Modeling (SEM), revealed that both cognitive and affective image aspects positively impact intentions to recommend and revisit, with the affective aspect exerting a stronger influence. Policymakers should prioritize promoting natural beauty and historical landmarks while enhancing local amenities to broaden Algeria’s appeal and attract more visitors. This comprehensive approach is essential for bolstering the country’s tourism sector in line with its development goals.

Deep Analysis of the Homogenization Phenomenon of the Ancient Water Towns in Jiangnan: A Dual Perspective on Landscape Patterns and Tourism Destination Images

Attractiveness plays a key role in the marketing activities that promote the sustainable development of tourist destinations. Many researchers have acknowledged that regional tourist destinations face challenges in attracting tourists due to homogeneity. Achieving sustainability of the ecological landscapes of tourist destinations, creating a unique image that differentiates them from other destinations, and fostering cooperative relationships among regional tourist destinations remain significant challenges. To investigate the reasons behind this and potential solutions to the homogeneity of tourist attractions in regional tourist destinations, our research team employed methods including landscape pattern analysis, tourist destination image analysis, principal component analysis (PCA), clustering algorithm (K-means), text analysis, word frequency analysis, sentiment analysis, and semantic network analysis. Through an in-depth examination of the homogenization phenomenon in the ancient towns of the southern Yangtze River, our research confirms that the spatial form of these ancient towns has become increasingly simple and scattered over time, with a concentration of the same land use types. Half of the sampled ancient towns had exhibited homogeneity in landscape patterns by 2020, with pronounced homogeneity observed in cultivated land, water networks, and hand-made land. Excessive commercialization has further contributed to the loss of local characteristics, resulting in homogeneity issues in the image of tourist destinations in the Jiangnan water towns and ancient towns. This article discusses the characteristics of homogeneity in the ancient towns of the Jiangnan water towns and their practical implications, offering valuable insights and experiences to tourism planners, designers, researchers, and other stakeholders engaged in the study of sustainable regional tourism destinations.

Clustering Travelers’ Lifestyle Destination Image from Five Asian Traveler-Generated Content

This study examines the destination image and lifestyle experience via traveler-generated comments. To understand the travelers’ behavior, we first established a crawler, which helps us to gather the travelers’ comments from tourism social media. After conducting a content analysis, text mining, and factor analysis of a sampling of 23,019 travelers’ comments, this study found that travelers based on their activities and experiences constructed their image. Additionally, we also found that the travelers’ emotions and impressions showed up with their images. From the result of factor analysis, we extract the 13 clustering results and perform the one-way ANOVA with Scheffe’s method to compare the difference among each group. Finally, we used the related sentences to draw a relation map to explain the inner difference between travelers. This study’s results suggest that traveler-generated comments can be especially useful for destination image analysis and market segments in tourism marketing and management. This study also highlights the importance of understanding destination image and marketing segment from the travelers’ comments and challenges for those in tourism marketing to narrow the gap.

Destination Image Analysis with User-Generated Content: A Computer Vision and Machine Learning Approach

This study proposes an approach to identify the elements that shape the destination image in the minds of potential tourists who peruse social network posts, based on Instagram images of Foça, a touristic district of İzmir, Turkey, in summer 2019. The elements in shared images that contributed to the development of Foça’s destination image were identified with computer vision, while word embedding and machine learning were used for element categorization and clustering, respectively. The study demonstrates the proportion of the elements in the photographs shared by Instagram users in 27 categories and subsequently, without any human intervention, constructs a representation of the elements the destination image was most dependent upon. Categorization showed that the categories that were the most representative of the destination image of Foça were sea and landforms, celestial, arts, events, urban, boating and water sports and constructional, respectively. The majority of destination image research with social network data sets relies on observation, interpretation or survey results. These studies are time-consuming and labour-intensive due to the large size of social network data. However, in this study, social network data can be analysed faster and efficiently with artificial intelligence and machine learning compared to conventional methods. Furthermore, the innovative methodology developed in the study contributes to the current tourism literature by proposing a decision support system for making computer-assisted tourism marketing decisions.

Analysis of Destination Image and Memorable Tourism Experience on Revisit Intention through Satisfaction of Tourist in Maimoon Place

This study aims to analyze the destination image and memorable tourism experience on revisit intention through tourist satisfaction at Maimoon Palace. The type of research used in this research is associative research. The population in this study were tourists who visited the Maimoon Palace with the samples taken in this study were 135 respondents. Withdrawal of the number of sample sizes is done by non-probability sampling using purposive sampling method. The data used are primary data and secondary data obtained through a list of statements and documentation studies. The data analysis technique uses structural equation modeling (SEM) with the Smart PLS version 3.0 analysis tool. The results of the study directly show that destination image has a positive and significant effect on revisit intention, memorable tourism experience has a positive and significant effect on revisit intention, destination image has a negative and significant effect on revisit intention, memorable tourism experience has a positive and significant effect on revisit intention, and tourist satisfaction has a positive and significant effect on revisit intention. Then the results of tests carried out indirectly indicate that tourist satisfaction is not able to mediate the relationship between destination image and revisit intention, but tourist satisfaction is able to mediate the relationship between memorable tourism experience and revisit intention. Keywords: Destination images, memorable tourism experience, tourist satisfaction and revisit intention.

Relationship Analysis of Destination Image, Sports Involvement, Event Quality and Travel Motives as an Antecedent Factors on Revisit Intention in Recurring Running Sports-Event

The research aimed to examine the relationship between destination image, sports involvement, quality of sport events, and travel motives as antecedent factors to predict repeat visit intentions of marathon events. This was a further study of the trends that occur in society regarding the changing perception that sport is not only for health purposes, but also as a profitable tourism subject. Data was collected using quantitative, non-probability, purposive sampling methods. The research instrument was an online questionnaire which was distributed to tourists and athletes who took part in running sport-events. The data analysis method uses Structural Equation Modelling with Smart PLS 3.0. The test results show that not all research hypotheses are accepted. Sport-event quality (SQ) has a positive relationship but has no significant effect on revisit intention (RI) from repeated sporting events. The positive effect of travel motive (TM) on the intention to revisit a repetitive sporting event (RI) is proven. Travel motive (TM) also has a positive and significant effect on destination image (DI). Sports involvement (SI) has a positive and significant effect on revisit intention (RI). The same positive and significant effect is also shown in the relationship between sports event quality (SQ) and destination image (DI) variables. Revisit intention (RI) of repeated sporting events was found to be positively influenced by destination image (DI). There is a significant indirect effect of sport-event quality on intention to revisit repeated sporting events (RI), mediated by destination image.

Developing an artificial intelligence framework for online destination image photos identification

With the development of advanced technologies in computer science, such as deep learning and transfer learning, the tourism field is facing a more intelligent and automated future development environment. In this study, an artificial intelligence (AI) framework is developed to identify tourism photos without human interaction. Adopting online destination photos of Australia as a data source, the results show that the model combining a deep convolutional neural network and mixed transfer learning achieved the best image identification performance. This study identified 25 image classification categories covering all the tourism scenes to serve as a foundation for future tourism computer vision research. The results indicate that the AI photo identification framework is of great benefit for the understanding of projected destination images and enhancing tourism experiences. This study contributes to the existing literature by introducing an intelligent automation framework to big data research in the tourism field, as well as by advancing innovative methodologies of online destination image analysis. Practically, the proposed framework contributes to the marketing and management of smart destinations by offering a state-of-the-art data mining method. • Develops an artificial intelligence framework to identify tourism photos. • Identifies 25 image classification categories covering all tourism scenes. • Introduces an intelligent automation framework in tourism big data research. • Offers a state-of-art data mining method to the management of smart destinations.

Analysis of Destination Image on the Intention of Tourists: A Reflection on the Role of Social Media Moderation in the Hotel Industry

رسانه‌ی اجتماعی به‌عنوان یکی از کانال‌های بازاریابی که به سرعت در حال گسترش است و در حال حاضر بیش از دو سوم از کاربران اینترنت را به خود اختصاص می‌دهد. از سویی دیگر، صنعت هتلداری یکی از ارکان اصلی فعالیت گردشگری به‌شمار می‌رود؛ به‌طوری‌که بسیاری از کشورها با سرمایه‌گذاری‌های هنگفت در بخش هتل‌سازی، به‌دنبال جذب گردشگران بیشتر هستند. هدف از پژوهش حاضر بررسی تأثیر تصویر مقصد بر نیت سفر گردشگران با توجه نقش تعدیل‌گری رسانه‌های اجتماعی در صنعت هتلداری است. جامعه آماری این پژوهش شامل كليه گردشگران مراجعه‌کننده به هتل‌های 5 ستاره تبریز می‌باشد. با توجه به نامحدود بودن جامعه آماري حداقل حجم نمونه در سطح اطمينان 95 درصد و دقت برآورد پنج درصد با استفاده از فرمول كوكران 384 نفر انتخاب شد. ابزار گرد‌آوری اطلاعات پرسشنامه استاندارد بوده که روایی آن توسط خبرگان تأیید شده و پایایی آن نیز با استفاده از ضریب آلفای کرونباخ برآورد شد. تحقیق حاضر براساس هدف یک تحقیق کاربردی و همچنین براساس چگونگی به‌دست‌آوردن داده‌های مورد نیاز، از نوع تحقیقات توصیفی و همبستگی می‌باشد. برای تجزیه و تحلیل داده‌های جمع‌آوری شده از روش‌های آماری توصیفی و استنباطی (پیرسون و آزمون رگرسیون) استفاده گردید. نتایج حاصل از آزمون فرضیه‌ها نشان می‌دهد که؛ تصویر مقصد تأثیر مثبت و معناداری بر نیت سفر گردشگر دارد و همچنین تصویر مقصد بر نیت سفر گردشگران با نقش تعدیل‌گری رسانه‌های اجتماعی مورد بررسی قرار گرفت.

A Review of Text Corpus-Based Tourism Big Data Mining

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years.

Analysis of Destination Image, Tourism Object, and Infrastructure Effect on Tourism Visiting Intention to Wamena (A Case Study of Tourism Development Planning in Wamena)

Wamena as one of the National Tourism Destinations (DPN) has high tourism potential, consequently it can be a locomotive for economic growth in Papua. However, Wamena still has struggled with infrastructure and it has to be prompted by creating destination image. This study is conducted to analyse tourists’ intention effect to visit Wamena. Sampling method used is random sampling through questionnaires and it is analyzed by using Partial Least Square. The results of data processing confirms that destinationi mage and tourism object have positive and significant effect on visiting intention, while infrastructure has negative and insignificant effect on tourism visiting intention to Wamena. Inadequate infrastructure is not a barrier for tourists to visit it. Beautiful scenery and fascinating destination objects are elementary generator for them to visit it. Thereby, tourism development can be implemented in Wamena as mountain or adventure tourism working with great physical. Approach should be committed by government or privates to cultivate potential tourism in Wamena through exposing its authentic culture and destination objects to promote best image destination. Keywords : destination image, tourism object, infrastructure, visiting intentions, Wamena DOI : 10.7176/JMCR/59-06 Publication date : August 31 st 2019

Image effect on customer-centric measures of performance

This study analyzes the effect of the difference between the pre-trip image of a destination and the post-trip image on, for the first time, two key elements of traveler behavior, which in turn are two customer-centric measures of destination performance: travel satisfaction and intention to revisit a destination. While the literature on the analysis of destination image has been prolific, the intricacies of the effects of changes in destination image on traveler behaviors remain unexplored, behaviors whose relevance is still greater when they show destination performance indicators. Based on the concepts of the zone of tolerance (derived from the service quality model) and loss aversion (from prospect theory), we explain the differential asymmetric effects of variation in destination image on intention to revisit and satisfaction, based on a sample of 12,024 individuals. Critical implications for destination marketing organizations are provided.

Analysis of the projected image of tourism destinations on photographs: A literature review to prepare for the future

Making the right impression is paramount to succeed in today’s very competitive market, where photographs have acquired a prominent role in doing so. The evaluation and analysis of destination image have been tackled mainly from the perspective of the tourists’ perceptions. However, the projected image of destinations has received limited attention in the literature and the topic has not yet been successfully operationalized. Moreover, existing literature on the projected image on photographs (PIP) is scattered and lacking of an integral approach. Thus, the aim of this article is to fill this gap by carrying a comprehensive literature review on the destination’s PIP, covering information sources analyzed, destinations included, number of pictures, time framework, methodology, and specially categorization (people, activities represented, and tourism context) and specific attributes used. As a result, this article provides researchers with a reference guide to understand the current situation of the research on this topic, context, methods, and focus of previous studies. Finally, it identifies trends and reflections on future research.

Factors Affecting Foreign Tourist’s Word-Of-Mouth toward Ho Chi Minh City-A Mediation Analysis of Destination Image

Factors Affecting Foreign Tourist’s Word-Of-Mouth toward Ho Chi Minh City-A Mediation Analysis of Destination Image

An analysis of destination image for emerging markets of Turkey

The purpose of this study is twofold: (1) to assess the perceived importance of the attributes that form the destination image (DI) based on general vacation experiences and opinions of travelers and (2) to compare the expected performance of Turkey’s attributes in attracting travelers from the emerging markets (i.e. China as well as Arab countries) using importance–performance analysis (IPA). The data were gathered from a sample of 426 prospective Chinese and Arab travelers during two prominent travel expositions in Dubai and Shanghai using face-to-face interviews with structured questionnaires. Comparative factor analysis and two generated IPA grids for each sample provided important insights into the perceived DI of emerging markets. More specifically, study findings provide valuable practical management information to destination management organizations in Turkey that want to design tailored marketing strategies for emerging markets. Further practical and theoretical implications are discussed in detail.

A detailed method for destination image analysis using user-generated content

Social media and user-generated content (UGC) have revolutionized tourism and hospitality communication and are seen as rich sources of information for destination image analysis. Many articles have been published about travel-related UGC, in particular, quantitative and qualitative content analysis of travel blogs and online travel reviews (OTR). Researchers have typically analysed small samples of population-representative travel diaries (tens or hundreds of files), which allow for manual processing. However, the enormous growth of OTRs requires operationalization through computerized methods, and the aim of this article is to propose a detailed method for semi-automatic downloading, arrangement, cleaning, debugging, and analysis of large-scale travel blogs and OTR data. This enables the classification of collected webpages by dates and destinations and offline content analysis of the text as written by the tourist. More than 130,000 useful trip diaries of tourists who visited Catalonia between 2004 and 2014 have been gathered, and significant results have been obtained in terms of content analysis in relation to destination image.

The importance of destination image analysis to UK rural tourism

PurposeThe Forest of Dean attracts relatively low numbers of staying visitors and low visitor spend. The paper aims to explore the image of this destination and identify the potential to visit along with any underlying factors that may deter visitors.Design/methodology/approachA mix of qualitative and quantitative methods were used to gather and analyse responses to an online survey. Respondents offered a range of words and pictures from which they were asked to select those they felt best represented the destination, and also then asked to explain their selection. Quantitative data were also analysed for frequency of occurrence. A content analysis of qualitative data was conducted to generate destination image clusters of the Forest of Dean.FindingsThe findings show there is a very positive and unique image associated with the Forest of Dean among previous visitors, with many expressing an intention to visiting the area again.Research limitations/implicationsAlthough response rates are relatively low, and results are heavily concentrated on actual visitors, the research provides very broad ranging and meaningful data that have offered a deeper and richer insight into the image of the Forest of Dean.Practical implicationsThe paper has provided important data from which new competitive brand image strategies can be developed for the destination. The paper also provides a first step towards a comprehensive image analysis for the Forest of Dean, from which future promotional strategies can be developed.Originality/valueMany visitor guides promote UK rural destinations on their superb scenery, relaxation, outdoor leisure activities, quality accommodation, locally‐sourced food, a range of attractions and a full calendar of events and festivals. To date there has been very little research carried out on UK rural destination brands and no academic research carried out on the image or branding of the Forest of Dean as a unique rural destination.

IMAGES OF THE PEARL RIVER DELTA TRAVEL DESTINATIONS IN CHINA

Being a popular topic in tourism research, destination image analysis has been widely covered in previous studies. However, few studies have ever discussed images and implications within a group of approximate or subregional destinations. This study examines tourists' perceived images of the Pearl River Delta (PRD) subregional destinations. Based on a survey of tourists who attended organized tours and visited the PRD subregion, a multivariate data analysis is used to identify the image dimensions of the PRD and to examine image differences among the subregional destinations. It is found that each destination may hold some strong or distinctive images; however, a small destination may find it hard to distinguish itself from its larger partners.

Destination image analysis—a review of 142 papers from 1973 to 2000

The analysis of destination image is relatively recent. However, in almost three decades since the first studies emerged, the topic has become one of the most popular in the tourism research literature. A review of 142 destination image papers, published in the literature during the period 1973–2000, was undertaken to provide destination image researchers with a reference guide to the context, method and focus of previous studies.

An Examination of the Role of Beneficial Image in Tourist Destination Selection

This article outlines the use of the so-called beneficial image as a framework for the analysis of destination image for decision to visit a country for a vacation. The beneficial image is conceptualized as the image characteristics of a destination that subsequently influence tourists’ decision to visit particular vacation destinations. The beneficial image characteristics are conceptualized under five value dimensions—functional, social, emotional, epistemic, and conditional—which are a part of consumption value theory. In addition, the development of measurement in destination image via a category-based approach is outlined. The proposed framework and method are used to assess the destination images of both Thailand and the United States. The findings of the study have demonstrated the effectiveness of the proposed conceptual and methodological frameworks, both at theoretical and practical levels.