Barriers and enablers to the acceptance of bioinformatics tools: a qualitative study

Abstract

One of the most important developments in the biomedical sciences today is the emergence of bioinformatics, the “science of managing and analyzing biological data using advanced computing techniques” [1]. The past decade has witnessed an explosion of biological data stored in large central databases as well as software tools to organize, visualize, and analyze the data [2–5], yet the acceptance and use of these applications by biologists lags behind this proliferation [6]. While some practices, such as the analysis of DNA and protein sequences, have fully diffused in the biomedical community, other bioinformatics practices still face adoption difficulties [7,8]. Medical libraries are increasingly required to provide services such as resources, training, occasional reference assistance, and individualized consultations to biomedical researchers [1,9–12] and have the potential to play a significant role in facilitating the acceptance and use of bioinformatics software by researchers. To provide effective services, medical libraries can benefit from gaining an understanding of the barriers and enablers to the acceptance of bioinformatics applications by researchers; however, at present there is a small body of literature on this topic [7–9]. A useful theoretical framework to study bioinformatics acceptance is Rogers' diffusion of innovations theory [13], adapted to the context of information systems by Moore and Benbasat [14]. Like other models of information systems diffusion [15–18], the framework suggests that perceptions of an information system play an important role in explaining end users' intentions to use a system and that intentions are predictors of actual use. Moore and Benbasat [14] propose eight perceptions, which are summarized in Table 1. Table 1 Perceptions of information systems innovations as defined by Moore and Benbasat [14] The authors previously studied the effect of hands-on training workshops, using either a structured step-by-step method or guided trial-and-error exploration methods, on end-user perceptions and intended use of bioinformatics tools for primer design and microarray data analysis [19]. Hands-on training positively affected perceived ease of use (PEOU) of the primer design tool; however, it decreased PEOU of the microarray data analysis tools. Surprisingly, intention to use both types of software decreased following hands-on training [19]. The present qualitative study was conducted to further increase understanding of the barriers and enablers to biomedical researchers' acceptance of bioinformatics applications, with a focus on the decision process underlying the selection of tools for primer design and microarray analysis and the long-term effect of training on these behaviors.