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This study proposes a new model by partially combining personality traits (PT) and Technology Acceptance Model (TAM) attributes to examine the influences of personality characteristics (conscientiousness, openness) and perception of technology (perceived usefulness, perceives ease of use) on e-purchase intention. We use truncate sampling technique and survey questionnaire to target the sample of Taiwanese online consumers and collect data. We find that consciousness (CON) (personality attribute) significantly influences perceived usefulness (PU) (technology perception attributes), perceived ease of use (PEOU) (technology perception attributes) and openness to experience (OPE) (personality attribute). PU, PEOU and OPE have significant impacts on e-purchase intention (INT). PEOU has the strongest positive impact on (INT). In addition, PU, PEOU and OPE combined together mediate the relationship between CON and INT. Further post hoc analysis of the mediation shows that both PU and PEOU are sustainable mediators. However, OPE is not a significant mediator.

The recent literatures indicate that the tourism development (TD) has significant influence over the environmental degradation of both high-tourist-arrival and lowtourist-arrival countries. This study investigates the empirical influence of TD on environmental degradation in a high-tourist-arrival economy (i.e. United States), using the wavelet transform framework. This new methodology enables the decomposition of time-series at different time-frequencies. In this study, we have used maximal overlap discrete wavelet transform (MODWT), wavelet covariance, wavelet correlation, continuous wavelet power spectrum, wavelet coherence spectrum and wavelet-based Granger causality analysis to analyse the relationship between TD and CO 2 emission in the United States by using the monthly data from the period of 1996(1) to 2015(3). Results indicate that TD is majorly having the positive influence over CE in short, medium and long run. We find the unidirectional influence of TD on CE in short run, medium and long run in the United States.

The traditional linear Granger test has been widely used to examine the linear causality among several time series in bivariate settings as well as multivariate settings. Hiemstra and Jones [19] develop a nonlinear Granger causality test in bivariate settings to investigate the nonlinear causality between stock prices and trading volume. This paper extends their work by developing a nonlinear causality test in multivariate settings.