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This study aims to explore switch intention of users’ reading behaviour. Amazon’s 2007 introduction of the Kindle e-book reader launched a new way of reading. The popularity of e-book readers has exploded over past several years. However, the Chinese e-book reader market is still relatively underdeveloped and underexploited when compared with North America or other regions. At the time, it remained unclear whether consumers could be persuaded to abandon conventional printed books for the new device in the Chinese market.

This research has gathered 352 valid questionnaire samples on an Internet survey from the general population. Path analysis was used to investigate the causal relationships which were hypothesized by the structural equation modelling methodology to identify key factors in intention to switch to the e-book reader.

The results indicate that convenience and social influence were found to have a significant effect on consumer switching intention, while switching cost was found to be a significant obstacle to switching. This research also found that convenience of carry and operation of new technological products will lower switching costs. Furthermore, it will lead consumers to have higher switching intention when surrounding friends and families have good experiences and impressions of using new products. Thus, the manufacturers and sellers of e-readers have to consider the easy-to-handle convenient-to-carry while designing products. And the concept of higher social influence and lower switching cost shall lead the marketing strategies.

Recently, some studies address what factors drive users’ willingness to use this new device for reading based on innovation diffusion theory or the technology acceptance model. Few studies have investigated adoption of e-readers for consumers’ perceived innovative and their perception of the costs. Hence, this paper studies the switching intention of readers through dimensions including new product attributes (compatibility, complexity, convenience and e-book content), social influence, price and switching costs and verifies the relationships among these dimensions on Chinese market.

The paper's aim is to provide information about mobile viruses for end‐users or organizations and recommend useful tips of how individuals can protect their mobile phones from the intrusion of mobile phone viruses.

This paper surveys chronicle, securities, risks and countermeasure of mobile phone viruses.

This paper investigates infection routes, threats, damage, and spreading ways of mobile phone viruses and provides available countermeasures. It also provides useful tips about mobile viruses, indicating what to do and how to do it.

This research may not be exhaustive about mobile viruses.

A very useful source of information and impartial advice for end‐users or organizations who are planning to protect their mobile phones and their own privacy.

This paper is the first to survey the risk of mobile phones and it discusses how organizations or individuals can protect mobile phones from the intrusion of viruses and how to develop their own mobile virus security plan.

An e‐mail virus is an e‐mail that can infect other programs by modifying them to include a replication of itself. When the infected e‐mails are opened, the e‐mail virus spreads itself to others. Today's society has seen a dramatic increase in the use of e‐mails. As a result, organizations must take even more precautions to guard against the introduction of e‐mail viruses into their systems. This paper discusses how organizations can protect their e‐mails from the intrusion of e‐mail virus and how to develop their own e‐mail virus security plan.

New malicious e‐mails are created at the rate of thousands a year and pose a serious security threat. Especially, new, unseen Internet worms and virus often are arriving as e‐mail attachments. In this paper, Bayesian probabilistic network is examined to detect new malicious e‐mail viruses through anomaly detection. Experimental results show a better malicious e‐mail detection using Bayesian probabilistic networks. Managerial implications on how companies can protect their e‐mails and develop their own e‐mail security plan are addressed as well.

Blogging has become part of a consumer's decision making process when shopping online; however, the understanding of blog recommendation's effect on consumer purchase decision is still vague. The purpose of this study is to examine whether the blog reader's trusting belief in the blogger is significant in relation to the perceived usefulness of the blogger's recommendations; and how the blog reader's perceptions influence his/her attitude and purchasing behavior online. The moderating effect of blogger's reputation on readers’ purchasing intentions is also tested.

Based on various theories, a model was proposed in this study. A survey involving 327 blog readers as participants was analyzed in the empirical study to investigate whether the usefulness of bloggers’ recommendations and trusting beliefs toward blogger had influence on consumers’ attitudes and behavioral intentions toward online shopping.

The results indicated that perceived usefulness of bloggers’ recommendations and trust had significant influential effect on blog users’ attitude towards and intention to shop online. Moreover, the findings showed that different determinants affected the users of perceived‐high‐reputation and perceived‐low‐reputation blogs.

The findings suggest bloggers’ electronic word‐of‐mouth (eWOM) to be a promising marketing strategy for increasing sales. The marketers should provide free trial products and services to the perceived‐high‐reputation bloggers who, as valued opinion leaders, will influence and prompt others to shop online through a trusting effect. As for perceived‐low‐reputation bloggers, the marketing strategists should strive to emphasize the usefulness of products and services being marketed, so these perceived‐low‐reputation bloggers can focus more on describing the advantages and benefits of products or services discussed in their blogs.

Although increasing numbers of users have begun to use social networking sites (SNSs), the user growth of a few SNSs continues to decrease. Therefore identifying factors that influence users' intention to adopt and continuously use a particular SNS is a critical issue. To explore the factors, this study aims to apply the theories of reasoned action, uses and gratifications, and innovation diffusion to explain why people continue to join SNSs.

The study participants were members of Facebook in Taiwan. An online questionnaire was used to conduct empirical research, and the data of 348 respondents were analysed using the partial least squares regression approach.

It was found that the reasons why people continuously use SNSs vary with different innovation diffusion stages. In particular attitude toward SNSs had the strongest direct effect on continuous intention while the impact of social norms was not significant in the different innovation diffusion stages.

From a practical perspective, insights provided by the study can help SNS developers understand user motivation and thus design more effective marketing strategies.

The proposed model provides an improved understanding of the needs of different SNS users, and testing verified the effects of the factors related to gratification and innovation diffusion.

Many video sharing sites (e.g. YouTube, Vimeo, and Break) host user-generated video content in the hopes of attracting viewers and thus profits. Therefore, continuous use and video sharing behavior on the part of site users is critical to the continue enjoyment of other users and to the video service providers business. The purpose of this paper is to provide an improved understanding of what motivates internet users to share videos and spend more time on video sharing web sites.

The authors propose a research model based on Uses and Gratification Theory and on Social Cognitive Theory, incorporating key determinants of web site stickiness. An online survey instrument was developed to gather data, and 265 questionnaires were used to test the relationships in the model.

The causal model was validated using SmartPLS 2.0, and 14 out of 18 study hypotheses were supported. The results indicated that continuance motivation and sharing behavior were important antecedents of YouTube stickiness and mediated the influence of need, personal, and environmental factors.

The proposed framework can be used by online video service providers to develop a platform that satisfies user needs and to enhance sharing intention.

The study provides a comprehensive framework of the antecedents and effects of continuance motivation and sharing behavior on video sharing web sites.

Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones).

The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling.

By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective.

This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification.

Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.