To persuade people to buy a product or service online, they must be visually convinced and attracted to use the sales website. Thus, there is need to understand how different user groups perceive various designs of websites ...

Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. However, ...

In an online marketplace, buyers rely heavily on reviews posted by previous buyers (referred to as advisors). The advisor’s credibility determines the persuasiveness of reviews. Much work has addressed the evaluation of ...

The increasing availability of enormous data about users online, along with availability of sophisticated tools and technology to store, aggregate, and analyze data for secondary use has raised concerns about how to balance ...

Once older adults become less self-sufficient, one or more adult children often assume the role of family caregiver. Unfortunately, this situation is usually perceived as stressful by caregivers. To better understand this ...

Research has shown that in Q&A social networks, collaboration between respondents results in quality answers. Since good answers are required to keep any Q&A social network active, it is important to understand the ...

In online marketplaces, many sellers highlight product and service information directly within product pictures for advertising purposes. Such a strategy increases the visual complexity of the picture and provides more ...

The issues of trust fraud, product genuineness and price dispersion jointly make Chinese C2C buyers difficult to identify trustworthy sellers with a low price. Little is known about the generation of initial trust when ...

Different classifications of Wiki editor have been proposed. However, so far there has been no mapping between user classes based on their contributions and their motivations, which can be useful to design persuasive ...

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” ...