The trend in female labour force participation: what can be expected for the future?

This study investigates the increase in the labour force participation rate of women. We estimate a binary age–period–cohort model for a sample of Dutch women born between 1925 and 1986. The results indicate that the increasing level of education and the diminishing negative effect of children have played an important role. Moreover, we find important unobserved cohort effects for pre-1955 generations, which is in line with results of studies on social norms. It is shown that the growth in female participation is likely to slow down in the near future, as such cohort effects are not relevant for younger generations.     

Strictness of leniency programs and asymmetric punishment effect

We analyze the impact of leniency programs on the behavior of firms participating in illegal cartel agreements in a two-stage repeated game model. Our approach takes into account asymmetric punishment effect and allows to discuss the design of leniency programs in the setting with asymmetries. The main contribution of the paper is that we consider heterogeneous firms. This heterogeneity results in additional costs in case of disclosure of the cartel, which are caused by asymmetric punishments. Next, following current antitrust rules, we analyze effects of the strictness of leniency programs, which reflects the likelihood of getting a complete exemption from fine even in case many firms self-report simultaneously. Our main conclusion is that leniency programs work better for small companies, since a lower rate of law enforcement is needed in order to induce self-reporting by smaller firms, while big firms are less likely to start a cartel in the first place given the possibility of self-reporting in future. Finally, we analyze optimal enforcement strategies of the antitrust authority and conclude that the authority with limited resources should implement more generous leniency rules the more cartelized the economy is.     

The multiple effects of carbon values on optimal rotation

Non-consumptive benefits which increase with crop age, like keeping carbon sequestered, lengthen optimal rotation compared with rotation for timber alone. High proposed carbon prices may extend rotation indefinitely. Carbon storage in wood products reduces this tendency. Biomass as an energy source displacing fossil fuels favours rotations near those of maximum biomass productivity. Use of sawn timber to displace structural materials with high embodied carbon favours somewhat longer rotations. Effects of rotation on soil carbon, and fossil carbon volatilised in harvesting operations, are further complications. Including all carbon effects results in optimal rotations somewhat longer than those based only on timber value, but shorter than those based on timber plus forest carbon. To include all factors intuitively is not possible: balanced appraisal needs economic calculations.     

A contrast analysis of online hotel web service purchasers and browsers

Ever since the introduction of Internet technology to general business, websites have played an important role in the business and promotional efforts of hotels. However, the existing tourism and hospitality literature contains only a limited number of articles that examine the perceived importance of the different attributes of a hotel website. This paper reports a study that analyzed the different perceptions of specific hotel website attributes between online purchasers and browsers and between Western and Asian users. The empirical findings reveal a set of attributes that are the most important in developing effective hotel websites. The findings also indicate the necessity of designing English- and Chinese-language hotel websites differently, based on the significant differences in the responses obtained from these two groups of online users.     

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.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.     

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 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.