The Effects of Explanation Mechanism on Recommended Information: An Example of Electronic News

• Main Authors: Chia-Feng Tsai, 蔡嘉鳳
• Other Authors: Her-Sen Doong
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/20055385318678235100

碩士 === 國立嘉義大學 === 資訊管理學系碩士班 === 94 === The Internet features, e.g. quickly updated and easy to browse have made the electronic news an important information source in the digital world. However, the convenience and low cost to publish or send messages on the Internet have created the critical issue of the information overload. As such, how to handle the essential information efficiently has received great attention from researchers and consumers. To meet this need, the famous electronic news Websites offered the recommend mechanism so as to help their readers to filter and select news. Via the data mining technology, the recommend mechanism was able to offer readers customized electronic news according to each reader’s user profile or browsing behavior. However, readers who did not understand how the recommend mechanism worked may doubt the recommended news was actually the advertisement manipulated by companies instead of a real recommendation from other readers. Consequently, readers may distrust the recommend mechanism, and may be annoyed by the recommended information. By introducing the explanatory mechanism, this study aimed to enhance reader’s perceptions towards the argument quality and information credibility of the recommendation mechanism. Ultimately, the readers’ trust towards the recommend mechanism may be enhanced and the reader’s intentions to adopt the information may also increase. The experiment method was applied in this study. In order to improve the research validly and reliability, the questionnaire was used to collect the quantitative data; whilst the think aloud protocol was used to collect the qualitative data. Findings showed that both the explanatory mechanism and recommended information source significantly (p<0.05) impacted on readers’ adoption of the recommended information. Though the explanatory mechanism could effectively influence the argument quality, it had no significant (p<0.05) influence on the information credibility. Further, both the argument quality and information credibility significantly (p<0.05) and highly explain the readers’ trust towards the recommendation mechanism. That is, via increasing the argument quality and information credibility, it was easier to persuade readers to trust the recommended information. The readers’ trust not only highly explained the readers’ adoption of the recommended information, but significantly (p<0.05) mediated the information credibility’s impact on readers’ intentions of the information adoption. Findings also indicated that, the explanatory mechanism effectively increase the readers’ adoption intension. Managerial implications of how B2C Website can sort out the problems of the information overload and customization lack were proposed.