Investigating the Predictors of Digital Library Engagement: A Structured Literature Analysis

Digital library is no longer considered a lavish use of technology but rather a mandatory need for users, especially postgraduate students. Each year, public and academic libraries invest a lot of money to subscribe to various digital resources, such as online databases, electronic books, electronic journals, and electronic magazines. However, many of these organizations have difficulties justifying their digital library investment due to lack of usage among the users. This underutilization of digital libraries is credited to the lack of digital library engagement among the users. Digital library engagement is defined as a deep and long-time use of digital library resources. Some researchers equate engagement to usage; however, engagement goes beyond the generic term of usage and may include multiple dimensions such as affective, cognitive, and behavioral. Previous works on the digital library have mostly focused on the generic definition of use instead of engagement. Researchers believe that the lack of study on digital library engagement causes the underutilization of digital libraries' resources. Despite the rising interest in the digital library, no research has studied digital library engagement predictors. Therefore, this paper aims to discuss the predictors of digital library engagement at the conceptual level. A structured literature review methodology was adopted; a total of 135 pieces of literature were included in this study. In total, 30 predictors of digital library engagement were identified, and these predictors were grouped into four factors: technological, individual, organizational, and contextual factors. Grounded on the previous literature on the digital library, the contributions of this paper are as follows: first, this paper identified a list of predictors of digital library engagement from past digital library literature, second, this paper categorized the predictors into four factors, and finally, this paper proposed a conceptual model for further validation of the predictors.