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.     

Spirituality and corporate social responsibility: an empirical narrative from the Balinese tourism industry

The emerging body of knowledge has indicated the positive relationship between spirituality and corporate social responsibility (CSR), yet our understanding of how spirituality can play a role in CSR in general, and in tourism in particular, remains limited. This research takes a qualitative case study approach to the role of spirituality-inspired CSR by providing an empirical investigation of tourism businesses in Bali, Indonesia, where the Hindu based Tri Hita Karana philosophy drives its cultural identity. A total of 20 qualitative interviews and two focus groups were conducted with Bali tourism stakeholders including businesses, government, community and NGOs. The findings show that spirituality plays an important role in CSR in Balinese tourism in three dimensions, namely (1) as a key element of the broader social and cultural context to create a conducive external environment for CSR; (2) as a CSR driver to inspire business leaders; and (3) as a complement to CSR governance, though with limitations. This research contributes to the literature by providing an empirical narrative that demonstrates strong multi-layered links between spirituality and CSR and the implications for tourism destinations in spirituality-oriented cultures.     

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.     

An application of the theory of planned behaviour to understand compliance with daytime running headlights law among motorcycle riders

Abstract

Fatal motorcycle crashes are a persistent problem in Southeast Asia. In many respects, road safety experts have acknowledged conspicuity issue as one of the main causes of such accidents. To mitigate this deficiency, daytime running headlights (DRHs) law has been documented among the widely exercised countermeasures. The present study aimed to explore socio-psychological factors shaping intentions towards use of DRHs among Malaysian motorcyclists, because compliance level with this rule has remained constant since 1996 (80%). For this purpose, a model was constructed through the Theory of Planned Behaviour (TPB) to evaluate factors affecting motorcyclists’ intentions towards compliance with DRHs law. A series of statistical analyses were performed to explain the effect of these factors and to predict degree of compliance with this rule. The model applied on a sample of university students (N?=?398) demonstrated that the TPB components could significantly predict motorcycle riders’ behaviours and degree of compliance with DRHs law. Moreover, it would be of assistance to policy-makers to recognize groups of motorcyclists more likely to cause an increase in traffic accidents. In general, regular educational campaigns were recommended to raise awareness regarding consequences of not using DRHs and also to nurture factors influencing motorcyclists’ attitudes towards DRHs.     

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.     

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.