The Influence of Affective Brand Experience Dimension on Brand Equity of the Smartphone Millennial Users in Malaysia

This study intends to highlight the power of affective brand experience dimension and link how it can influence brand equity of smartphone users in Malaysia. Measurement items for affective brand experience dimension and brand equity were developed by integrating existing literature and qualitative in-depth interviews with students who own and use a smartphone. Therefore, 359 usable questionnaires were returned. Data were analyzed using PLS-SEM to test the influences of affective brand experience dimension on brand equity. The study found that affective brand experience dimension is an important factor influencing brand equity of smartphone users in Malaysia. The study provides evidence that the affective brand experience dimension positively influences brand equity. The distinctive contribution of this research is that it examines the influence of affective brand experience dimension on customer-based brand equity in the context of smartphone brands in the Malaysian emerging markets. Such work is essential in understanding the importance of experiential marketing in an emerging economy such as Malaysia for building a strong smartphone brand.

Creation of Digital Content as a Service Learning in Teaching Foreign Language in High School

This article includes a study of a teaching foreign language program based on service learning and the creation of digital content in learning Spanish as a foreign language course in higher education.The study embraces the design of a theoretical framework for the reproduction of projects,such as the one described here,as well as the development of the following educational bases:reflection and critical thinking,digital competence,emotional and affective dimension,service,participation and cooperation.

Affective rating ranking based on face images in arousal-valence dimensional space

In dimensional affect recognition, the machine learning methods, which are used to model and predict affect, are mostly classification and regression. However, the annotation in the dimensional affect space usually takes the form of a continuous real value which has an ordinal property. The aforementioned methods do not focus on taking advantage of this important information. Therefore, we propose an affective rating ranking framework for affect recognition based on face images in the valence and arousal dimensional space. Our approach can appropriately use the ordinal information among affective ratings which are generated by discretizing continuous annotations.Specifically, we first train a series of basic cost-sensitive binary classifiers, each of which uses all samples relabeled according to the comparison results between corresponding ratings and a given rank of a binary classifier. We obtain the final affective ratings by aggregating the outputs of binary classifiers. By comparing the experimental results with the baseline and deep learning based classification and regression methods on the benchmarking database of the AVEC 2015 Challenge and the selected subset of SEMAINE database, we find that our ordinal ranking method is effective in both arousal and valence dimensions.