Combining time-series and econometric forecast of tourism activity

It has always been difficult to model the travel industry because tourism involves such a diverse set of activities. However, various regional decision makers have become increasingly interested in predicting the flows of visitors through their market. Accurate forecasts of the number of tourists' arrivals, their length of stay, and their expenditures improve planning and inventory control. Stochastic time-series models have compared favorably with econometric models at the aggregate level while some naive automatic forecasting tools have fared well in comparison when predicting industry-level behavior. Several approaches have been developed to improve forecast accuracy. This paper presents parsimonious methods of improving accuracy by combining various forecasting techniques. The Box-Jenkins stochastic time-series method is combined with a traditional econometric technique to forecast airline visitors to the State of Florida.     

Forecasting tourist arrivals in Barbados

Importance of forecasting in tourism is not a controversial issue. Recently, there has been increased attention on forecasting models in tourism. The value of a forecasting model depends on the accuracy of out-of-sample forecasts. At present, there is no indication as to which model or class of models is suitable for forecasting tourism. This paper specifies, estimates, and validates an ARIMA model for forecasting long-stay visitors in Barbados. The accuracy of the short-term forecasts surpasses most recent forecasting studies. The implication of the study is that customized model building may be highly rewarding in terms of accurate forecasts compared to standard or simple methods.     

Visitor Attitudes to Stonehenge: International Icon or National Disgrace?

Stonehenge is the UK's major prehistoric tourism attraction, with almost 1 million annual visitors, one of the top 10 UK visitor attractions since the early 1990s and an international icon used in tourism marketing. However, Stonehenge is controversial; major issues are access, authenticity and interpretation, with the actual visitor experience being criticised and termed ‘a national disgrace’ by British Members of Parliament (MPs). This paper reports on research conducted at Stonehenge in late summer and early autumn 2004. This involved a questionnaire survey that asked about motivations, visitor impacts and satisfaction levels. Results reveal educational factors to be major motivators and that Stonehenge is a World Heritage Site was also an important motivation. The results reveal a variety of views, and a more complex picture than is apparent from much rhetoric concerning Stonehenge as a visitor attraction. Overseas visitors were more positive in their reaction than British visitors, first time visitors more satisfied than repeat visitors and female visitors somewhat more positive in their reaction than men. In terms of their experience, and largely contradicting the UK Government committee claims, the majority of visitors indicated that Stonehenge has good interpretation, a fair entrance charge, provides good value for money and, overall, is an enjoyable experience.     

Impact of decomposition on time series bagging forecasting performance

Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.     

National destination pledges: Visitor management through emotional engagement,commitment and interpretation

National destination pledges are a destination and visitor management intervention that seeks to positively influence visitor behaviour. They have been used since 2017, and the earliest and most prominent among these are the Icelandic Pledge, the Palau Pledge, New Zealand's Tiaki Promise, and the Sustainable Finland Pledge. Using semi-structured interviews with 17 destination managers and marketers, covering all aforementioned pledges, this paper demonstrates that national destination pledges are a new and innovative soft visitor management intervention. In comparison to other soft visitor management techniques, national destination pledges strategically connect with the emotions of visitors, require (often public) action from visitors, and engage visitors in a process of interpretation to build their own understanding of their role in destination management. As such, this paper outlines how these pledges offer a unique approach to destination management by engaging with demand side stakeholders (visitors).     

Forecasting China's Monthly Inbound Travel Demand

Summary

China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonally and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naive “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.     

Alone with others: The paradox of privacy in wilderness

Attitudinal studies of wilderness visitors have indicated that people seek opportunities to limit interaction with other visitors so as to achieve privacy and solitude. This conventional interpretation of wilderness recreation was evaluated by comparing measures of visitor attitudes and social behavior in the backcountry of Yosemite National Park. Results show no association between visitor attitudes toward crowding and observed social interaction or behavior to avoid such social interaction. These findings suggest that subjective responses of visitors measured by questionnaires and interviews are often of debatable validity. Greater validity can be achieved by avoiding reliance on common‐sense interpretations in theory formulation and by employing multiple measurement techniques.     

EXPLORING EXPENDITURE‐BASED SEGMENTATION FOR RURAL TOURISM: OVERNIGHT STAY VISITORS VERSUS EXCURSIONISTS TO FEE‐FISHING SITES

About three fourths of new small tourism businesses fail within the first few years of operation mainly due to poor identification and lack of knowledge of their markets. While understanding visitor spending pattern is critical for the market to be economically viable and successful, research on rural tourist spending behavior is lacking in the literature. Therefore, the purpose of this study was to explore a visitor segmentation approach based on rural visitor spending behavior. Multiple regression analysis suggested accommodation attribute as the most useful predictor for visitor spending. Two subgroups were developed using accommodation type as a segmentation criterion—overnight stay visitors versus short excursionist. Comparative analyses were done using socio‐demographic variables, trip behavior, recreational motivation, and activity preferences. Overnight stay visitors and short excursion group showed significant differences on several behavioral and motivation variables. Practical suggestions are provided for attracting tourists to rural tourism destinations.     

Computers—tourism applications

A survey of State Tourism Offices (STOs) in the USA and Canada reveals that 84% are currently using in-house computers, with microcomputers the most popular. Pauline Sheldon, Assistant Professor in the School of Travel Industry Management, University of Hawaii, describes how new software is most needed for information retrieval for visitors. Research activities such as forecasting, market analysis, and visitor expenditure analysis lend themselves easily to computerization. Information specialists who can adapt generic software to the needs of STOs are found to be most useful. Service bureaux are used mostly for economic and social impact studies and visitor motivation studies.     

Visitor monitoring along roads and hiking trails: How to determine usage levels in tourist sites

Best-practice visitor-monitoring techniques are crucial for the assessment of tourism-related impacts in natural areas of high conservation value. In such studies, ecosystem variables are typically compared between high and low usage tourist sites. We assessed visitor use at 80 sites in the Flinders Ranges gorges and compared 11 visitor variables for their potential to differentiate usage levels between sites either exposed to vehicle or hiker traffic. We show that the efficiency with which a visitor variable represents usage levels depends on the access mode to gorges, with the number of passing visitors best suited for monitoring of usage levels in gorges permitting hiker access only, and variables describing camping usage best suited for gorges permitting vehicle access.Further, the distinct advantages and disadvantages of four visitor-monitoring techniques were examined; namely, the direct monitoring of visitor use by staff observers, the assessment of proxy variables from which past and present use can be inferred, GPS tracking of visitors and the survey of visitors by an interview-based questionnaire. We recommend GPS tracking because of the reliability and detail of data and the many sites per day that can be sampled. Due to a strong, positive correlation, the campground size and the number of fireplaces may be recorded in proxy of the camper numbers to increase time-efficiency and robustness of measures against short-term fluctuations in usage. Survey data gathered in relation to specific site-use were tempered by the memory of visitors and their ability to describe or reference the visited sites on a map. Visitor surveys were therefore useful only as a supplementary method for differentiating usage levels on a coarser spatial scale.     

A visitor flow management process for touristified archaeological sites

In recent decades the number of visitors to archaeological sites has increased exponentially. This increase has generated both negative and positive impacts. This article proposes a Visitor Flow Management Process (VFMP) that aims to minimize the negative impacts and enhance the benefits visitor flows can have in touristified archaeological sites. Although several proposals for visitor management frameworks exist for natural protected areas, for archaeological sites there are only isolated strategies and actions. Following a review of the literature on the visitor impacts and visitor management strategies implemented in 11 touristified archaeological sites around the world, 96 visitor management measures were identified, classified and synthesized into a three stage-process: (1) Restrict the number of visitors; (2) Redistribute visitor flows in time and space and (3) Interpret the archaeological site considering the mass influx of visitors. VFMP is a useful tool for heritage site managers dealing with mass tourism, whose implementation can contribute to reducing damage to heritage and enhancing the quality of the visitor experience.     

The evaluation of visitor experiences using the peak-end rule

ABSTRACT

As the peak-end rule suggests that the peak and the end moments are a better predictor of the overall evaluations of experiences than an average of the total experiences, the purpose of this study was to examine the role of the peak-end rule in predicting overall satisfaction with the visitors’ experiences. The data was collected from 187 visitors who participated in alleyway historical tour areas. The results of this study support for the peak-end rule of visitor experiences associated with travel and tourism behavior. The study also suggests distinct roles for the visitors’ positive experiences of enjoyment, happiness, and pleasure with the demographic variables to explain the visitor satisfaction. Additionally, the research finding implies that the visitor experiences were enhanced at the peak and end moments rather than at the most memorable episodes during the historical tour activities. Tourism practitioners may provide a range of meaningful opportunities and differentiated programs with the participants by identifying the relationships between the peak-end experience and visitor satisfaction. Future research can focus on investigating other important antecedents of individuals’ experiences, and identifying the complex nature of visitors’ perceptions and their relationships with their experiences.     

Forecasting tourism demand: Developing a general nesting spatiotemporal model

This study proposes a general nesting spatiotemporal (GNST) model in an effort to improve the accuracy of tourism demand forecasts. The proposed GNST model extends the general nesting spatial (GNS) model into a spatiotemporal form to account for the spatial and temporal effects of endogenous and exogenous variables as well as unobserved factors. As a general specification of spatiotemporal models, the proposed model provides high flexibility in modelling tourism demand. Based on a panel dataset containing quarterly inbound visitor arrivals to 26 European destinations, this empirical study demonstrates that the GNST model outperforms both its non-spatial counterparts and spatiotemporal benchmark models. This finding confirms that spatial and temporal exogenous interaction effects contribute to improved forecasting performance.     

Micro-mobility patterns and service blueprints as foundations for visitor management planning

This paper proposes the use of micro-mobility patterns and service blueprints in visitor management planning. It argues that such planning approaches can improve management outcomes as well as visitor experiences whilst adding efficiency to the relevant management processes. The paper is based on the findings of visitor research on visitor flows and perceptions of visitor management in a nature-based tourism attraction in Wellington, New Zealand. These findings are used to adapt a service blueprint for the overall attraction to separately reflect visitor experiences of international visitors and New Zealanders. The paper posits that it is thus possible to identify and subsequently address the visitor management requirements of different visitor groups. Implications are discussed at three levels; first, for the case study attraction; second, for tourism attractions more broadly; third, conceptual implications for visitor management research are considered. Specific findings include the differences in micro-mobilities found across different market sectors, the need to improve signposting to offer distance and time guidance, the importance of topography, the potential to spread usage pressures across sites and the future potential to use mobile GPS units to obtain more detailed information.     

Tourism forecast combination using the CUSUM technique

Forecast combination in tourism has emerged as an important research area due to its relevance to tourism decision making. This paper further investigates the impact of forecast combination on forecast accuracy by applying a quadratic programming approach to determine the combination weights for individual forecasts. In particular, we introduce three novel ideas which have not been found in previous tourism forecasting studies. First, we introduce a quality control technique, CUSUM, to determine the time for updating the weights. Next, we develop a hybrid method (using quadratic programming) to combine the forecasts to reduce forecasting errors. Thirdly, we investigate whether different performance measures yield different results. Thus, instead of comparing different weighting methods using only one performance measure, we introduce several indicators for performance comparisons. The empirical results suggest that the controlled weighting method both saves time in updating the combination weights and improves the overall performance of the combined forecasts. The method is also easy-to-implement and should be used to improve forecasting accuracy in practice.     

Forecasting tourism demand with ARMA-based methods

The forecast of tourism volume in the form of arrivals is of special importance for tourism and other hospitality industries because it is an indicator of future demand, thereby providing basic information for subsequent planning and policy making. In this paper, three univariate ARMA-based models are applied to tourism demand, as represented by the number of world-wide visitors to Hong Kong, Japan, Korea, Taiwan, Singapore, Thailand, the Philippines, Australia and New Zealand. The study employs both monthly and quarterly time series generated from nine principal tourist destinations in Asian-Pacific region in the forecasting exercise to ensure the reliability of the forecasting evaluation. Forecasting performance based on disaggregated arrival series in a particular destination is examined as well. The general impression is that the ARMA-based models perform very well and in some cases the magnitude of mean absolute percentage error is lower than 2% level.     

Visitor Motivational Factors and Level of Satisfaction in Wellness Tourism: Comparison Between First-Time Visitors and Repeat Visitors

This study aims to examine the differences between visitor motivations and satisfaction between first-time visitors and return visitors to a recreation wellness tourist attraction site in South Korea. Data were collected using a questionnaire at a national arboretum and 573 samples were used for analysis. The study discovered that first-time visitor motivations were composed of four factors: “relaxation and rest”, “novelty”, “self-exploration”, and “accessibility”. The return visitor motivations were “sightseeing and experience”, “convenience for touring”, “self-exploration”, and “accessibility”. The desired outcomes motivating the visitation and level of satisfaction were different between first-time and return visitors. The results contribute practical information to managers and marketers not only of the arboretum, but also of wellness tourist attractions to improve strategic visitor management in Korea and beyond. The study strategically applies the arboretum as a competitive resource in a wellness tourism industry with strategic segmentation of visitors.     

Forecasting hotel-industry performance

This paper provides a method which may be used by hospitality managers to forecast the annual hotel-industry occupancy rate for their respective localities. An empirical application of the method demonstrates that it can generate reasonably accurate forecasts of annual industry performance and can be useful to managers in their evaluation of the future competitive environment. An assessment of the influence of key variables upon forecast accuracy suggests that market research on trends in visitor characteristics would be useful for longer-term forecasting.     

Assessing the asymmetric impact of interpretation environment service quality on museum visitor experience and post-visit behavioral intentions: a case study of the National Palace Museum

This paper examines the asymmetric positive–negative relationship between interpretation environment service quality, museum visitor experience, and post-visit behavioral intentions. The researchers collected 406 valid questionnaires from visitors to the main exhibit building in the National Palace Museum. Results suggest that the asymmetric effect differs depending on the attribute being examined. When visitors reported their perceptions of personal and sociocultural interpretation environment services, negative perceptions had a larger impact on museum visitor experience than positive perceptions. Moreover, results suggest that museum visitor experience functions as a mediator between perceptions of interpretation environment service quality and post-visit behavioral intentions.     

Forecasting tourism: a combined approach

In this article, we employ a combined seasonal nonseasonal ARIMA and sine wave nonlinear regression forecast model to predict international tourism arrivals, as represented by the number of world-wide visitors to Singapore. Compared with a similar study of the accuracy of international tourist arrivals forecasts by Chan (Journal of Travel Research, 1993, 31, 58–60)¹ and Chu (Journal of Travel Research, 1998, 36, 79–84)² using other univariate time series models, our proposed model has the smallest mean absolute percentage error.