Search results

The purpose of this paper is to identify the mediating effect of fan-page followers’ engagement activities and moderating role of followers’ demographic profile and trust level on their purchase intention.

This study utilised the customer engagement behaviour and consumer involvement theory as a foundation to explore the impact of variables. Structural equation modelling was utilised to test the model with the data collected from 307 Facebook fan pages’ followers of five Malaysian companies.

It was shown that following fan pages will influence fan page engagement, which in turn affects purchase intention and social media connectedness. Further analysis indicated that the impact of “follow” and “engagement” on purchase intention differs between genders, ages, level of trust and income.

The study serves as a basic fundamental guideline for academics and researchers to interpret the concept of following fan pages and engagement actions and its effects on purchase intention and social media connectivity, as well as opening a vast area of unexplored researches on the subject of social media.

The research provides information for business-to-consumer companies in utilising fan page based on user categories.

This study proposes the application of an empirically tested framework to the fan-page follow actions. The authors argue that this framework can provide a useful foundation for future social commerce research. The results would help academics be aware of fan page and its user’s engagement actions, which will provide a new avenue of research.

This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.

We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.

The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.

This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.

This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.

In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).