The contributions of this study to library research are threefold. Firstly, this study is a pioneering effort in explaining the behavioral intentions toward the newly emerging genre of academic e-books, which has recently become much more available. Secondly, the main aim of this paper is to investigate and compare the influence of recommendation sources on customers’ behavioral intentions to use e-books in an academic digital library. Finally, according to the findings, this research inferred that an increased perception of trust and decreased perception of risk can mediate the relationship between recommendation sources and customers’ behavioral intentions to use e-books in an academic digital library.
In addition, past research has not widely applied combined DRSA and flow network graph to predict the behavioral intention of e-book usage, especially in the context of social networks. Thus, this research presents a new approach to identifying e-book decision rules that infer the antecedents of the intent to use e-books under the effects of different recommendation sources. The result of the DRSA is a set of decision rules that can be used to explore undiscovered rules and characteristics. The decision rules can thus be transferred into a flow network graph, used for modeling a flow of information as a set of decision rules and explaining the corresponding flow network in terms of flow distribution. The flow network graph is a bridge for connecting the pathway of decision rules and the degree of their interdependency. It is a useful tool and approach to use for exploring and discovering the path dependences of decision rules, which can permit e-book-related organizations to derive and test predictions about how recommendation sources efforts contribute to behavioral intentions.
In light of its empirical implementation, this research shows that the DRSA is robust and not difficult to apply, particularly in the areas of forecasting and classification decisions, because this approach does not require the pre-specification of a functional form or the
creation of any particular statistical distribution assumptions about the variables of a model.
Practically, librarians should strengthen their e-book advantages (e.g., create easy search and index tools and build positive evaluations) to receive positive recommendations if users follow all of the recommendations of a source. They can also create online discussion forums to provide a usage-intention discussion; this can influence users’ perceptions of trust and risk and increase their willingness to use e-books.
The advantages of combined DRSA with flow network graph in the recommendation sources are summarized in two points. The first point is that the e-book publishers and academic libraries can discover hidden information in terms of recommendation sources and predict and act upon that new information based on scale information. The second point is that such a model will be welcomed for its ability to capture the effect of recommendation sources’ efforts on behavioral intentions and turn the information into useful marketing strategies, eventually improving the usage of e-books in academic libraries.
A few issues remain to be addressed. One limitation of this study is that the investigation of the behavioral intentions to use e-books is relatively new to library researchers. The discussed findings and their implications were gathered from a single study that targeted a specific user group in Taiwan. Thus, continued research is needed to generalize the findings.
In addition, further discussion regarding other user groups besides students would be beneficial. Another limitation of this research is that the data collection was cross-sectional.
That is, it measured perceptions and intentions at a single point in time. However, perceptions often change over time as individuals gain more experiences (Mathieson, Peacock, and Chin, 2001). This tendency towards change has implications for researchers and practitioners who are interested in predicting e-book usage intentions over a longer period of time.
In sum, several implications were drawn in this study. Firstly, a better understanding of the relationship between recommendation sources and the behavioral intentions of using
e-books in academic libraries may further contribute to developing marketing strategies. For instance, librarians should strengthen e-book advantages (e.g., create easy search and index tools and build positive evaluations) to receive positive recommendations if users follow all of the recommendations of a source. They can also create online discussion forums to provide a usage intention discussion, which can influence users’ perceptions of trust and risk and increase their willingness to use e-books.
Furthermore, understanding the characteristics of users is important for academic libraries. Collecting and analyzing users’ background information, such as gender or daily Internet usage, can provide abundant information about customers that decision-makers can use to characterize customers for strategic planning and decision-making purposes and increase customers’ usage of certain products. For instance, in this research, the results show that female users rely on WOM recommendation sources for their behavioral intentions in using e-books, whereas male users depend on experts’ recommendations.
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Appendix A
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