Search results

The purpose of this paper is to develop the scale of destination image (SDI) to assess destination image affecting the consumption associated with tourism.

The scale was developed through four steps: review of literature, formulation of a preliminary scale, confirmatory factor analysis (CFA), and examination of predictive validity by a structural equation modeling (SEM) analysis. The preliminary scale consisted of 32 items. Employing a systematic sampling method, a total of 199 research participants responded to a mail survey.

In the CFA with maximum likelihood estimation, four factors with 18 pertinent items are retained. This four‐factor model displays good fit to the data, preliminary construct validity, and high reliability. The SEM analysis reveals that the SDI is found to be positively predictive of tourism behavioral intentions.

This paper develops an original multi‐dimensional 18‐item scale measuring destination image from the perspective of tourists, which can provide academicians and practitioners with a reliable and valid analytical tool to assess destination image.

Destination management is in urgent need of analytical and policy tools, and even more so in the context of tourism for development programs. Understanding both structural elements and dynamic processes are essential. This chapter describes a model of destinations which considers three types of structural components: factors/resources, attractors (of tourism demand), and support systems. It analyzes as well the optimizing behavior of destination stakeholders, both endogenous and exogenous, as a way to understand destination dynamics. The model can be applied in the strategic positioning of destinations as well as in achieving competitiveness and sustainability—ultimately contributing to development—through tourism policy plans and governance processes. The model was born in the context of a European Eureka–ComTur research project, and has been tested in a variety of destinations.

This research aims to model the existing system of destination competitiveness, identifies leverage points and develop revised model to achieve sustainable competitiveness.

System dynamics is used as method of modeling destination competitiveness. Structure of model utilizes 9-factor model as reference. Leverage points are identified using system archetypes. Revised model is built with resource-based view (RBV). Case study was conducted in Samosir, Toba Lake. Data used are secondary data and results of in-depth interviews.

There are 3 sub-systemic characteristics (archetypes) that hinder competitiveness. They are limit to growth quality gap, fix that fails infrastructure and promotion, tragedy of common lake pollution. Destination was unable to meet tourist expectations. Tourists spending decreased, demand size was small. Industries are unable to increase capabilities. Professionals, entrepreneurs, local workers, supporting industries are less interested in entering industry. Government policies do not match with destination's needs. Lake as main attraction is getting polluted. To achieve sustainable competitiveness, destination must utilize their valuable, rare and inimitable (VRI) resources and capabilities to design unique experiences for tourists, hence sustainable.

Government policy should be shifted to prioritizing development of valuable, rare, inimitable and well-organized resources and capabilities of destination, to produce unique tourist experience and achieve sustainable competitiveness.

Methods and findings, combining system dynamics, system archetype, 9-factor model and RBV to achieve sustainable competitiveness is novel and can enrich tourism sustainable competitiveness theory/concept.

This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.

Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.

This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.

This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.

The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.

Sustainable tourism development.

This study finds the expansion of new theory competitiveness of ecotourism destinations.

One of the most well-known, seminal models in the tourism field is the one proposed 50 years ago by Stanley C. Plog. His venturesomeness model has been widely cited in journal articles, textbooks, and has also been used as a reference for planning and designing tourism marketing projects. However, empirical research using Plog's psychographics has yielded varied results, some of which have corroborated his model, while others have found partial or no support for some postulates. Therefore, the purpose of this chapter is to offer an exhaustive review of 47 studies in the literature which have employed Plog's venturesomeness concept to examine travelers' personality traits, attitudes, and behavior, synthetizing empirical findings and drawing conclusions from the cumulative results. A discussion of the model's contribution to the current body of knowledge, managerial implications for tourism practitioners, and directions for future research are presented.

The purpose of this paper is to provide a comprehensive review of the literature and develop a model of the determinants, indicators and effects of destination competitiveness (DC), as well as several propositions.

This study thoroughly reviewed extant literature to develop a conceptual model and propositions.

Two key findings are listed below. First, 12 different environmental factors are identified and 12 propositions are developed linking these environmental factors to DC. Second, a new indicator of DC is developed, namely, Tourism Attractions-Basics-Context (TABC) model. The TABC model is simple and directly taps into the benefits tourists seek in a destination.

Directions for future research are discussed in detail in the paper.

Managerial implications are discussed in detail in the paper.

The extant research on the topic of DC has been rather fragmented and incomplete in scope. The research presented in this paper addresses these limitations.

Given the lack of research on the nomological validity of tourism destination consumer-based brand equity (CBBE) constructs incorporating core, well-established constructs from the travel and tourism discipline, this research investigates the influence of venturesomeness as a moderator in a model with destination image, satisfaction, and overall CBBE as antecedents of return intentions.

The study uses online panel data of past visitor to the sea-side destination of Corpus Christi, Texas. A sample of 210 residents in Texas and surrounding states was employed to estimate the hypothesized effects through partial least squares-structural equation modeling (PLS-SEM).

Results demonstrate the predictive effects of destination CBBE dimensions on tourists' revisit intention, with the significant moderation effects of venturesomeness through its influence on tourist satisfaction.

Findings provide general support to the nomological validity of the proposed model, highlighting the role of satisfaction as a central dimension to explain destination loyalty, the limitations of generic scales to investigate tourism destination contexts, and the incorporation of consumers' psychographics and lifestyle variables on destination CBBE.

Destination marketers should develop segmentation strategies to target travelers with psychographic profiles that are more responsive to the factors that foster CBBE.

This research provides insights on the nomological validity of a CBBE model by evaluating its integration with a context-specific theoretical domain, which is a condition to increase the explanatory scope of theoretical relations and claims in intermediate theory, and to move the research field forward.

The purpose of this paper is to examine the direct and indirect effects of destination image on destination loyalty. Indirect effects are measured via tourists’ satisfaction.

A reliable conceptual framework was developed through a mixed methods research methodology. A three-pronged approach was used to cross-validate the results from a literature review, expert interviews, and exploratory study. Structural equation modeling was used in conceptual model development and hypotheses testing.

Exploratory dimensions of destination image, satisfaction and destination loyalty of Indian cities were revealed from Thai tourists’ perception. The positive indirect effects of destination image on destination loyalty via satisfaction is supported.

This study is limited to tourists of Thai nationality who visited India’s travel destinations. Future research should include further generalization of the research methodology to specific travel destination.

This study provides useful information for India’s tourism industry, specifically for the marketing of historical cities as travel destinations.

By proposing a reliable conceptual model based on a mixed methods research methodology, this study is among the first to explore destination image, satisfaction and destination loyalty in India’s tourism context. The mediating role of satisfaction on destination image and destination loyalty was also recognized as an extension of, and contribution, to the theoretical foundation for the destination image concept.