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Mediating role of brand app trust in the relationship between antecedents and purchase intentions-Iranian B2C mobile apps

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Journal of Management Analytics 2020年第1期7卷 69-104页

作者： Mohammad Mahdi Movahedisaveji badiea shaukat Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

This study investigates the mediating role of brand app trust in the relationship between brand app antecedents and purchase intentions via a brand mobile *** explores the mediating potential of brand and platform trust in this relationship between antecedents and purchase intentions and also highlights which brand antecedents are useful for directly or indirectly establishing trust towards purchasing via a mobile app in the Iranian mobile commerce *** study uses survey data collected through an online questionnaire form and applies the partial least squares method to extract its *** of the estimation results are found to be unbiased and *** results show that brand app trust plays a mediating role in the relationship between the brand app antecedents of word-of-mouth recommendation,subjective norms,perceived image(social antecedents),mobile computing self-efficacy(consumer-based antecedent),and perceived ease of use(system antecedent)and purchase intentions via a brand mobile app in Iran.

关键词： brand app antecedents brand trust brand app trust platform trust purchasing intention via mobile app social commerce Iran

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Individual and combination approaches to forecasting hierarchical time series with correlated data:an empirical study

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Journal of Management Analytics 2019年第3期6卷 231-249页

作者： Hakeem-Ur Rehman Guohua Wan Azmat Ullah badiea shaukat Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

Hierarchical time series arise in manufacturing and service industries when the products or services have the hierarchical structure,and top-down and bottomup methods are commonly used to forecast the hierarchical time *** of the critical factors that affect the performance of the two methods is the correlation between the data *** study attempts to resolve the problem and shows that the top-down method performs better when data have high positive correlation compared to high negative correlation and combination of forecasting methods may be the best solution when there is no evidence of the *** conduct the computational experiments using 240 monthly data series from the‘Industrial’category of the M3-Competition and test twelve combination methods for the hierarchical data *** results show that the regression-based,VAR-COV and the Rank-based methods perform better compared to the other methods.

关键词： hierarchical time series individual forecasting methods combination forecasting methods correlation

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