Using Learning Taxonomy to Enhance Understanding of Innovation Adoption

Abstract

Innovators, early adopters, majority and laggards are components of what is known as Innovation Diffusion Theory (IDT) and represent groups as they adopt a new innovation. Education professionals have likely heard of Bloom’s Revised Taxonomy (BRT), which represents the loose progression from basic to advanced cognition in a learning process. These two theories are rarely discussed together and that is unfortunate because of the time and cost significance of too frequent failed implementations of new innovations. IDT identifies training and knowledge transfer as important components in knowledge, persuasion and decision stages of the innovation adoption process. However, previous research did not answer an important question: How do different adopter groups demonstrate various levels of cognition in the process of the adoption of a new innovation? In an attempt to investigate this issue, this research looked at the adoption of Reference Management software by academics to explore the possible relationship between IDT and BRT. A Canada-wide online survey was conducted with 462 participants consisting of graduate students and faculty. Data were analyzed with descriptive statistics, Principal Components Analysis and correlation procedures. A thematic analysis of qualitative semi-structured interviews with 12 respondents gave the findings additional depth. Three significant findings emerged. One, demonstration by the respondents of higher order functions in the software was correlated to the demonstration to lower order functions as theorized by BRT’s progression of cognitive processes. Two, the degree of innovativeness of the participants’ correlates to mastery of both basic and advanced functions. Three, laggards, in terms of adoption, demonstrate less mastery of the basic features and functions of an innovation implying that different IDT groups respond differently within BRT cognition levels. The implication of these findings is that training effectiveness in the supporting of the adoption of a new innovation is not solely dependent on either the training design or principles of BRT, nor is it solely influenced by the factors involved in the diffusion of an innovation. Together, these findings inform us to how we can use BRT and IDT in the knowledge transfer component of supporting the adoption of an innovation than commonly used current practices.