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.     

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.     

Embedding Ethics: Dialogic Partnerships and Communitarian Business Ethics

Although emotion has become one of the most popular research areas within organizational scholarship, few studies have considered its connection with unethical behavior. Using dual-process theory, we expand on the rationalist perspective within the field of behavioral ethics by considering the process through which two discrete emotions, anger and guilt, influence unethical behavior. Across two studies using different methodologies, we found that anger increases unethical behavior whereas guilt reduces unethical behavior. These effects were mediated by impulsive and deliberative processing. Overall, our results shed light on distinct mechanisms through which emotions can influence unethical behavior. Both theoretical and practical implications are discussed.     

An item response theory approach to constructing and evaluating brief and in-depth financial literacy scales

We applied item response theory (IRT) to construct and evaluate new brief and in-depth financial literacy scales. A survey of a UK adult sample (N = 589) included 50 questions to assess knowledge about managing financial resources and competence in using personal finance-related information—including five widely used items, on interest rates, inflation, investment diversification, mortgages and bonds. IRT applied to a scale of these items identified some limitations, overcome via further iterations to construct a new brief scale with sound psychometric properties. IRT was then applied iteratively to our pool, resulting in an in-depth, 20-item scale, also psychometrically sound, covering four broad financial domains: everyday money transactions; the concept of money; borrowing; and saving and investment. Parallel 10-item sub-scales were also evaluated. The validity of the new scales was demonstrated by regression analyses which found that, controlling for demographic variables, financial literacy predicted key indicators of financial well-being.     

Evaluating museum visitor experiences based on user-generated travel photos

ABSTRACT

This research introduces online travel photos published on social media platforms as a complementary data resource to examine the behavior and experience of museum visitors. The practical value of online travel photos is demonstrated through a case study of popular Hong Kong museums, particularly by using the photo content and metadata available from the Flickr platform. The proposed approach is a generic method for understanding museum visitor behavior and preferences, and supports museum practitioners in developing improved products for visitors. The case study findings are particularly beneficial for tourism managers, especially those in Hong Kong, in promoting and attracting tourists to visit local museums.     

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.     

Decision making for business model development: a process study of effectuation and causation in new technology-based ventures

This study investigates the decision-making logics used by new ventures to develop their business models. In particular, they focussed on the logics of effectuation and causation and how their dynamics shape the development of business models over time. They found that the effectual decision-making logic was used dominantly to generate a viable value proposition for a specific customer segment. Causal logic is then used dominantly to define the other business model components in relation to the value proposition and customer segment. When a shortage of resources emerges, causal logic is replaced by an increase in effectual decision-making again. They concluded that before investing significant resources in a business model it was crucial for firms to reduce, as far as possible, technological and market uncertainty through effectual strategies to avoid high re-configuration costs later.     

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.