Recommendation Sources on the Intention to Use E-Books in Academic Digital Libraries

博士 === 國立交通大學 === 管理科學系所 === 98 === Few library studies have investigated recommendation classifications for e-book (electronic book) usage, whereas none have directly compared which recommendation sources (word-of-mouth recommendations, advertising recommendations, and expert recommendations) might...

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Bibliographic Details
Main Authors: Chin, Yang-Chieh, 金揚傑
Other Authors: Lin, Chiun-Sin
Format: Others
Language:en_US
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/52222696472865029431
Description
Summary:博士 === 國立交通大學 === 管理科學系所 === 98 === Few library studies have investigated recommendation classifications for e-book (electronic book) usage, whereas none have directly compared which recommendation sources (word-of-mouth recommendations, advertising recommendations, and expert recommendations) might influence e-book usage intentions. To fill this gap in the literature, the main purpose of this study is to examine how users perceive the influence of recommendations in their intentions to use e-books for academic purposes. Data for this study were collected from 382 academic digital library users between the ages of 18 and 25. Firstly, a multiple regression analysis was conducted to identify the key causal relationships. A comparison of three recommendation sources (word-of-mouth recommendations, advertising recommendations, and expert recommendations) revealed that word-of-mouth (WOM) recommendations played a more important role than other recommendations did in determining the intention to use e-books in an academic digital library. The results also showed that enhancing the perceived trust and reducing risk in the use of e-books can mediate the relationship between recommendation sources and customers’ behavioral intentions to use e-books. The second analysis of this study is grounded in the taxonomy of induction-related activities using the dominance-based rough set approach (DRSA), a rule-based decision-making technique, to infer the behavioral intention of using e-book decision rules; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of behavioral intentions. Practical and research implications are also offered.