The purpose of this study is to explore the factors that inform a consumer's behavioural intention to adopt disruptive information technologies. This study uses an inductive reasoning approach to collect qualitative data which is analysed using interpretive analysis guidelines. The outcome of the analysis is the discovery of three new variables, namely, understanding of technology, meta-price value, and perceived need. The knowledge of technology refers to what a consumer knows about disruptive information technology. The meta-price value refers to a consumer's perception of the benefits to be gained from disruptive information technology as compared to those of the disrupted technology. The perceived need refers to a consumer's realisation of the extent to which disruptive information technology is the sole provider for a fundamental requirement. This study is, arguably, the first to propose a different model to explain the adoption of disruptive information technologies.
TopIntroduction
“Uber has always been controversial. Combine a business plan based on upending an entrenched industry with a CEO as aggressive as Travis Kalanick, and conflict is a given” (Lashinsky, 2017). Such is the nature of disruptive information technologies – literally overthrowing traditional business models. Some examples of such technologies are the sharing economy (Henten and Windekilde, 2016; Sovani and Jayawardena, 2017), 5G and internet-of-things (French, 2016), 3D printing (Kothman and Faber, 2016), mobile apps (Tribunella and Tribunella, 2016), ebooks (Frederick, 2016), big data (Schermann et al., 2014), simulation games (Smith, 2007), and internet computing (Carlo, Lyytinen and Rose, 2009). In addition to upsetting existing business models, these information technologies have the potential to challenge existing theories, explanations, and knowledge about them. Despite this potential, academic research is yet to catch up with exploring and providing an understanding of what factors inform individuals’ adoption of disruptive information technologies. Unfortunately, trade and industry magazines, and newspapers are at the forefront (see Pines, 2016; Jackson, 2017; Lashinsky, 2017; Steinmetz and Vella, 2017). Academic studies related to disruptive technologies have focused on advanced issues such as value co-creation (Camilleri and Neuhofer, 2017), minimising firm-level risks arising from disruptive technologies (Cheng et al., 2017), how to take advantage of disruptive innovation (Perez, Paulino and CambraFierro, 2017), changes in industrial structures (Henten and Windekilde, 2016), and how to strategically deal with disruptive technologies (Evans, Ralston and Broderick, 2009). While these studies are valuable, they raise several concerns for research concerning the adoption of disruptive information technologies.
First, they are mostly based on issues at the organisational level (e.g. Obal, 2017; Perez, Paulino and CambraFierro, 2017), industry level (e.g. Henten and Windekilde, 2016; Momeni and Rost, 2016; Cheng et al., 2017) or country level (e.g. Bakke and Henry, 2015). Second, they do not speak of what informs individuals' adoption of disruptive technologies. Third, they neither advance nor contribute to the theory of adoption of disruptive technologies. Fourth, they collectively suggest a fixation on developed country contexts (e.g. Zhu, So and Hudson, 2016; Vecchiato, 2017) and innocuous neglect of the adoption of disruptive technologies in developing country contexts. The effects are that we lack knowledge of factors which make individuals adopt or reject disruptive technologies, i.e. a theory of adoption of disruptive technologies; and literature from developing country contexts. This study is a response to these observed gaps. Thus the purpose of this study is to explore the factors that inform individuals’ behavioural intention to, and subsequently adopt disruptive information technologies. Qualitative data collected from young people – who are usually early adopters of new information technologies – were analysed using interpretive analysis methods. The outcome of the analysis is the inductive discovery of three variables, knowledge of technology, meta-price value, and perceived need. These new variables are used to propose a model for the adoption of disruptive information technologies.