A multi-criteria assessment of tourist farm service quality

Farm tourism is a significant means of supplementing farmers' incomes. This paper presents a methodology for ranking tourist farms by using a multi-criteria model based on the qualitative multi-criteria modeling methodology, DEX, to assess service quality. The software tool DEXi was used to achieve this end. The model was then applied to seven tourist farms with data derived from questionnaires completed by tourist farm operators and guests. The results are shown as service quality assessments for individual farms. The potential of the model for assessing the farms is demonstrated with the aim of providing a comprehensive explanation and justification of the assessment technique. It also indicates potential improvements that farms can make through “what-if” analysis and visualization. Despite limitations, such as use of qualitative data only, the approach is proposed as being both appropriate and advantageous when compared with other means of ranking enterprises.     

Factors influencing Chinese tourist flow in Japan – a grounded theory approach

This study examined the factors influencing Chinese tourist flow in Japan. The study used a grounded theory to obtain insight into the tourist flow through text analysis of 96 online travel diaries and used these data to propose a model of factors. The model constitutes five categories: tourist condition (travel purpose, time budget, travel companion, prior visit experience, and opinions of others), destination characteristic (destination resource and distribution of destinations), transportation characteristic (transportation expense and transportation network), macro environment (visa policy and political relationship), and unforeseen circumstance (weather condition and fortuitous event). The study established a model for comprehensive understanding factors influencing tourist flow at inter-destination level.     

Tourism Brochures: Usefulness and Image

Tourist information has been shown to have an important influence on the choice of vacation destinations. This article studies brochures, their significance as image generators, and their influence on the selection of destinations. The study sought to discover which features in this medium are more relevant in image generation and destination choice using logistic regression analysis. The analysis is based on data collected from tourists in Madrid, Spain. The results establish a model of usefulness of brochures in order to propose recommendations for their design and content. Implications pertain to the development of theoretical understandings about the influence of the information sources on destination image, destination choice, and satisfying tourist needs.     

Tourism's contribution to the economy of Kenya

Between 1963 and 1980, tourism was one of the top three domestic exports of Kenya, along with coffee and tea. In evaluating an export industry, its linkages with other sectors of the economy, its import content, and its role as a source of domestic income and employment should be considered. This paper attempts such an evaluation of Kenya's tourist industry. Between 1968 and 1976, linkages between the tourism sector and domestic agriculture and food processing were improved. The tourist industry was not particularly import intensive in terms of intermediate goods when compared to the economy as a whole. Employment in and wages paid by the tourist industry were below what is expected when compared to tourism's share of GDP.     

Forecasting tourist arrivals with machine learning and internet search index

Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.     

Tourists and retailers' perceptions of services

This paper describes a study which measures tourist and retailers' perceptions of service levels in a tourism destination. A service quality model was used to develop survey items and interpret the results. The service dimensions used to evaluate a tourism experience included reliability, responsive, assurance, and access. The results indicate tourists evaluate tourism services based on “who” delivers as opposed to the nature of the services (as outlined in the Service Quality model). This has implications for those who create and service tourism destinations, such as city planners and leaders, tourism convention and visitor bureaus, and retailers.     

Tourist market segmentation with linear and non-linear techniques

The need for in-depth knowledge of tourist market segments and the need to overcome the limitations of using linear techniques to analyse non-linear relationships requires a re-assessment of generally used approaches such as cluster analysis and multiple linear regression. The objectives of the research are (1) to consider the use of self-organising (SOM) neural networks for segmenting tourist markets and (2) to analyse the predictive ability of backpropagation (BP) neural networks for classifying tourists from follow-up surveys by using the output provided by a SOM neural network. The findings of the SOM neural network modelling indicate three natural clusters. In addition, the predictive ability of the BP neural network model appears to be superior to that of MLR static filter and logistic regression models. The BP neural network model developed for this application appears suitable for deployment (i.e. classification of tourists from follow-up surveys).     

Tourism and immigration: Comparing Alternative Approaches

Using survey data from ex-Soviet Union tourists visiting Israel and eligible for immigrant status, this article investigates alternative factors which affect the probability of a tourist making an immigration decision. The developed tourism-immigration model is based on predictors associated with push-pull migration, social capital, and tourism theories. The results of cumulative logistic regression models suggest that the probability of an immigration decision is based on a set of mutually reinforcing factors: well-established and supportive relatives at the destination along with intentions to own property and engage in business activities. Motivations are found as primary contributors to explaining and predicting a probable immigration decision.     

Effects of location on Airbnb apartment pricing in Málaga

The relatively recent social movement known as the “sharing economy” is becoming increasingly visible in online peer-to-peer platforms. One such platform is Airbnb; an accommodation marketplace which provides access to tourist accommodation. These platforms are having an important social and economic impact on tourist destinations such as Málaga, Spain. This paper presents a spatial econometric hedonic model that explains the pricing strategy of Airbnb tourist apartments in Málaga using factors such as host, guest, structural characteristics, and location. In relation to locational factors, the model in this study confirms the impact of spatial spillover effects, accessibility to certain amenities, traffic noise, walkability, and the ethnicity of residents in the neighborhood where the listing is located.     

Geographic factors affecting tourism in Zambia

This paper discusses several geographical factors critical to tourism planning and development in Zambia. The paper focuses on the size, seasonality of climate, and population distribution of the country as well as the location of tourism resources and distances between tourist attractions. Individually and collectively, these factors have a major influence on the levels of occupancy in hotels and game lodges, and air and land transportation. The infrastructural development and its utilization for tourism are evaluated, and a number of constraints to tourism development are also examined. A discussion of appropriate tourism policies highlights the delicate relationship between economic and environmental factors. This study concludes that Tourism Master Plans should be more cognizant of geographic as well as economic factors when developing destinations such as Zambia.     

A framework of tourist attraction research

Although tourist attractions are fundamental to the very existence of tourism, there have been few attemps to come to terms with the breadth of approaches that have been employed in their study. An examination of research methods used in the study of tourist attractions and the tourist attractiveness of places reveals that most studies can be classified into one or more of three general perspectives: the ideographic listing, the organization, and the tourist cognition of attractions. Each of these perspectives shares a distinct set of questions concerning the nature of the attractions, as expressed through the typologies used in their evaluation. At the same time, all three perspectives make comparisons based on the historical, locational, and various valuational aspects of attractions. This framework can be applied in the comparison and evaluation of tourist attraction related research.     

百度指数与旅游景区游客量的关系及预测研究——以北京故宫为例

网络搜索数据记录了用户的搜索关注与需求,为研究旅游经济行为提供了必要数据基础。文章基于百度指数,以北京故宫为例,利用计量经济学中的协整理论和格兰杰因果关系分析了百度关键词与北京故宫实际游客量间的关系,建立了没有百度关键词和加入百度关键词的两种预测模型并进行了预测精度比较。结果表明：故宫实际游客量与百度关键词存在长期均衡关系和格兰杰因果关系：加入百度关键词后的自回归分布滞后模型的样本期内的预测精度比没有百度关键词的ARMA模型提高了12．4％,样本期外的预测精度提高了14．5％。运用带有百度关键词的模型可以实现利用当天及滞后1～2天的百度指数数据预测故宫当天的游客量,不仅增强了预测的时效性,还可以更加及时、准确地为故宫景区管理部门提供决策的依据。     

DEVELOPING A FRAMEWORK FOR ASSESSING RESILIENCE OF TOURISM SUB-SYSTEMS TO CLIMATIC FACTORS

Some researchers have argued that resilience is a useful concept for understanding impacts on social-ecological systems, such as tourist destinations. This paper presents the development of a resilience framework for tourist destinations with a particular focus on climatic disturbances or stress, and their impacts on tourism activity sub-systems. Building on the model of a ‘stability landscape’, this research uses primary data from the Queenstown-Wanaka destination in New Zealand to evaluate the concept of resilience and to formulate surrogates that describe the factors that shape resilience. Challenges for future research, including the proactive building of resilience, are outlined. This research represents a good example of an interdisciplinary approach that transcends existing epistemologies in tourism.     

Delightful tourism experiences: A cognitive or affective matter?

There are many times that we, as tourist consumers, have been more than satisfied. We have been delighted with the product or service received. However, do we know the factors which affect our delight as customers? Are they affective or cognitive factors? The objective of this article is to empirically identify delighted consumers and the factors associated with these memorable tourist experiences when the threshold of satisfaction is exceeded. To this end, the authors used structural equation modelling to test a model based on a sample of 400 tourists obtained through a survey.The findings revealed that a consumer's positive affective state of delight seems to be affected largely by cognitive-affective antecedents. Specifically, the cognitive dimension encompasses three main factors of the tourist service: the customer-service interaction, the staff and the availability of the service. However, the tourist's happiness, as the main affective dimension, is also emphasised.     

Tourism's regional development impacts : A comparative analysis of the Greek islands

This paper explores the conditions that determine tourism development and theorizes how these conditions lead to a variety of impacts on host communities. Thirty-eight Greek islands were statistically analyzed using secondary data and a smaller sample was studied in greater detail through a comparative case-study approach. While the quantity of tourism development was found to be subject to demand, its quality and integration with other sectors were associated with the size of the community. Tourism impacts were found to be beneficial in larger islands. In smaller islands with high tourist activity, while a reverse in declining demographic patterns has been observed, tourism was found to create unstable, short-term, and dualistic development. The paper concludes that factors such as the local institutional capacity to absorb development and the potential interaction of locals and tourists should be considered in the making of tourism policy.     

我国旅游目的地居民对旅游影响感知的实证调查与分析

本文以全国范围内的23个旅游目的地为例,通过问卷调查,获取一手数据资料,运用SPSS13.0软件进行数理统计,分析了旅游目的地居民的旅游影响感知及其差异,得出了六大旅游影响感知因子.研究结果表明:现阶段我国旅游目的地居民的正面感知普遍占主导地位;个人内在因素是导致感知差异的主要因素;旅游目的地居民大致可分为三类:具有大局观的积极支持者、矛盾的理性支持者和关注自我的积极支持者.     

Ticket prices and revenue levels of tourist attractions in China: Spatial differentiation between prefectural units

Ticket pricing is a key issue for tourism research in China. Owing to regional differences, a unified nationwide price reduction strategy for tourist attractions would be inappropriate for China. To assist in regional ticket pricing decisions, this study uses ArcGIS spatial analysis methods to analyze the spatial differentiation of ticket prices and revenue levels between prefectural units, using 2017 data from 9450 A-grade tourist attractions. The results reveal prominent characteristics of spatial differentiation in tourist attractions’ average ticket prices, as well as in the proportion of their revenue from ticket sales, between prefectural units. Ticket prices are generally low in north China and high in south China; meanwhile, the proportion of ticket revenue is generally high in east China and low in west China. The factors influencing such spatial differentiation are tourism resources, local socio-economic conditions, and the management of tourist attractions. The findings help stakeholders to make differentiation strategy.     

旅游地居民环境行为研究综述

旅游地居民环境行为研究是旅游情境下环境行为研究不可或缺的环节。本文从研究进展、研究内容和研究方法3个方面出发,对旅游地居民环境行为的国内外研究进行梳理和分析。研究发现：（1）旅游地居民环境行为研究起步较晚,大致经历了萌芽（2002—2004年）、起步（2005—2013年）和发展（2014—2020年）3个阶段,国内外研究成果均较少,国外研究成果领先于国内;（2）旅游地居民地环境行为的概念界定尚未统一,测量量表的开发也较为薄弱;影响因素探究是旅游地居民环境行为的研究热点,但形成机制探讨相对匮乏,并缺少旅游地居民环境行为的预测研究;（3）旅游地居民环境行为研究方法较为单一,数据搜集方法以问卷调查为主,半结构式访谈为辅,数据分析方法以结构方程模型为主。基于此,分别从研究视角、研究内容和研究方法上剖析现有研究不足,指出旅游地居民环境行为未来研究方向,以期为旅游目的地环境管理提供有益指导。     

旅游流空间数据获取的基本方法分析——国内外研究综述及比较

有效、准确的旅游流数据获取是决定旅游流研究科学性的关键问题之一.文章通过对国内外旅游流相关研究中的旅游流空间数据获取方法的梳理,认为旅游流数据获取的基本方法有观察法、旅游者时空账户、现代追踪技术、二手面板数据和其他途径等5种,通过成本、效率、可操作性、拓展性和准确性等方面对5种方法进行了比较和述评,并对比国内外相关研究的差距与不同.文章指出,目前国内旅游流研究仍存在数据获取方法单一、现代技术应用不足、重视程度不够等问题,尚未实现与国际研究的全面接轨.文章旨在能够引起旅游管理部门和研究者对旅游流数据获取的重视,以科学、有效的途径收集准确、适用的数据,提高旅游流研究的科学性和对旅游业发展实践的指导意义.