mLearning as a softer visitor management approach for sustainable tourism

With increased visitation to protected natural areas over the last four decades, there is a need for implementation of effective visitor management strategies at these sites to mitigate visitor impacts. This study explores the application of mobile learning (mLearning) in environmental interpretation and visitor education within the context of conservation and sustainable tourism. Specifically, it proposes a conceptual framework for mLearning as a visitor management tool for sustainable tourism. Current developments and innovations in mobile broadband networks, smartphone technology, and mobile software applications present opportunities for the utilization of such mobile-driven applications in interpretive programs to encourage free-choice learning and mindful visitor experiences. If effectively implemented, such interpretive programs and mLearning applications can affect visitor perceptions, attitudes, and future intentions toward conservation and environmental protection.     

Examining the efficacy of self-classification approach in segmenting special-interest tourists: food tourism case

Self-classification is used as an a priori approach to tourist typology and market segmentation. However, skepticism still surrounds its ability to incorporate the multidimensionality of tourist behavior. This study seeks to empirically verify the efficacy of a single-item self-classification approach. The robustness of this self-classification measure is examined by comparing it to a data-driven multidimensional psychographic approach in terms of its ability to predict the behaviors of tourists toward food-related destination consumption. Results suggest that the single-item self-classification approach performs equally well as the psychographic approach in segmenting food-related consumption behaviors. The implications and limitations of this study are also discussed.     

A green economy indicator framework for tourism destinations

This paper presents a green economy indicator framework for tourism destinations which has been developed in the case study destination of Bali, Indonesia. Whilst the term “green economy” can have many interpretations, here it refers to the global strategy framework surrounding Rio+20, as well as the policies and strategies being developed by tourism destinations as a response. Many uncertainties remain about the effectiveness of these efforts and how they may be measured. For lesser developed countries in particular, reliable data is often difficult to obtain and this study uses a case study approach to identify the relevant, and measurable, indicators in this context. First, a nominal group technique was applied to identify the green economy issues for local tourism stakeholders. The indicators were then selected based on the green economy literature and a review of the available secondary data for the destination. Data on greenhouse gas emissions from tourism was identified as one of the critical gaps and an estimate is provided to show how this could be obtained and monitored. While this indicator framework was developed specifically for Bali, the case study may be relevant for many other island destinations in the lesser developed world that are experiencing rapid tourism growth.     

A fuzzy comprehensive evaluation algorithm for analyzing electronic word-of-mouth

This study evaluates tourism experiences shared through electronic word-of-mouth (eWOM) across four Chinese attractions. The objective is to develop a framework for evaluating eWOM by constructing an indicator system and implementing an analytic hierarchy process with the use of a fuzzy comprehensive evaluation algorithm. This framework is achieved by mapping more than 6000 websites related to Chinese tourism attractions and filtering over 200,000 useful reviews to measure service performance. Results indicate that ecological–biological attractions failed to make tourists feel “very satisfied” in various aspects, such as overall evaluation, infrastructure, traffic, natural environment, and social environment. Overall, the study contributes by presenting a framework that can be adopted by tourism researchers and industry practitioners to understand tourist preferences and evaluate service performance to improve service quality.     

Identifying changes and trends in Hong Kong outbound tourism

Despite the numerous research endeavors aimed at investigating tourists’ preferences and motivations, it remains very difficult for practitioners to utilize the results of traditional association rule mining methods in tourism management. This research presents a new approach that extends the capability of the association rules technique to contrast targeted association rules with the aim of capturing the changes and trends in outbound tourism. Using datasets collected from five large-scale domestic tourism surveys of Hong Kong residents on outbound pleasure travel, both positive and negative contrasts are identified, thus enabling practitioners and policymakers to make appropriate decisions and develop more appropriate tourism products.     

The impact of the Asian financial crisis on Japanese demand for travel to Hong Kong: A study of various forecasting techniques

The Asian financial crisis has drawn worldwide attention because of its significant economic impact on local economics, especially on the economy of a tourism‐dependent destination. Unfortunately, there have been very few articles about the relationship of the Asian financial crisis and tourism demand forecasting. This relative lack of prior studies on the Asian financial crisis and tourism demand forecasting is particularly true in the context of Hong Kong. This article reports on a study that utilized officially published data to test the accuracy of forecasts of Japanese demand for travel to Hong Kong, measured in terms of the number of Japanese tourist arrivals. Seven commonly‐used tourism forecasting techniques were used to determine the forecasting accuracy. The quality of forecasting accuracy was measured in five dimensions. Experimental results indicated mixed results in terms of forecasting accuracy. Overall, artificial neural network outperformed other techniques in three of the five dimensions.     

Are the best of the best better than the rest? The effect of multiple rankings on company value

In this paper, we explore the cumulative and interactive effects from being listed on one or more of four popular annual surveys (Fortune’s “Most Admired Companies” and “100 Best Companies to Work For,” Business Ethics “Best Corporate Citizens,” and Working Mother’s “100 Best Companies for Working Mothers.”) We find portfolios constructed of firms selected across these surveys add value to a portfolio, initially and over longer-holding periods, but the overall results are driven by the performance of those firms selected from the Most Admired Companies and Best Corporate Citizens rankings. We also discover that being listed in two or three different surveys on a yearly basis produces incremental value.     

European and Asian resistance to the use of the American case method in management training: possible cultural and systemic incongruencies

In this contribution to the use of the Harvard-style case-style method in the training of managers, a transfer problem is identified. This is manifested in the difficulties presented for many non-American students in the use of traditional case-studies. As a result, an example of a revised case method is proposed which offers more educational flexibility and better acceptability.     

The U.S.-China Exchange Rate Debate: Using Currency Offer Curves

The paper introduces an innovative graph for presenting bilateral exchange rates. The currency quantities are on the axes and the exchange rate is the ratio between them, i.e., the slope of a ray from the origin. Behavior is captured by currency offer curves. The paper uses the model to address the issues surrounding China’s export-led growth strategy, i.e., its policy of undervaluing the yuan.     

The sources of the Kuznets relationship between road fatalities and economic growth

This paper reports the results of an empirical analysis of the Kuznets curve relationship between per capita income and road fatalities across 60 countries over the period 1972-2004. This relationship hypothesizes that the number of road fatalities increases with increasing motorization in the early stages of economic growth. Eventually, due to advances in technical, policy and political institutions, it declines as per capita income increases. The quality of political institutions as well as improvements in medical care and technology are hypothesized to impact road fatalities. Results indicate evidence of a Kuznets curve relationship between per capita income and road fatalities for both highly developed and less developed countries and support our hypothesis that changes in institutional quality and medical improvements underlie the Kuznets relationship. The evidence presented in this study suggests that lowering corruption levels as well as improvements in medical care and technology would help to reduce road fatalities.