• The Journal of Industrial Distribution & Business
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• v.8 no.5
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• pp.15-21
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• 2017

Purpose - This research aims to examine the effects of 3D online products presentation by comparing it with general presentation. Research design, data, and methodology - To rigorously investigate the differences of effects between 3D presentation and general presentation, this study examines the differences of the two presentations in vividness, informedness, entertainment, product trust, and purchase intention. This research designed two different forms of online product presentations, 3D and general presentation for an experiment. Results - The research findings indicate that 1) the vividness of online product presentation has a positive impact on the informedness and entertainment. While both vividness and informedness product presentation have a positive impact on product trust, the entertainment of online product presentation has no significant impact. 2) Vividness, informedness, entertainment, product trust, and purchasing intention showed significant differences between 3D and general product presentations. 3) Overall, 3D product presentation showed a stronger impact on purchasing intention than the general product presentation. Conclusions - This research expands the area of business presentation by comparing the differences of 3D and general presentation methods. This study made a great contribution to theory development, and also to guidelines for practice. These insights could be used by organizations in developing realistic environments for business presentations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.196-212
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• 2014

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier's bias and achieves more reliable recognition results.