Guest post by Dr Justin Connolly from Dublin City University
Regardless of its benefits, an innovative technology platform such as MIDAS will only be effective if adopted. It is understandable therefore that passive resistance to innovation is a consistent concern in the literature as it is consistently identified as a salient reason for the failure of new technologies and systems in an organisational context.
While previous research has explored the reasons for user resistance, gaps remain in our understanding of exactly how users, such as health policy decision makers, evaluate the change related to adoption of a new innovation and then on the basis of that evaluation make the decision whether or not to adopt and continue to use it. In particular, missing in the explanation of health technology adoption decisions is the concept of status quo bias, that is, that user resistance can be due to the bias or preference to stay with the current situation.
Status quo bias explanations are considered (Samuelson and Zeckhauser, 1988) to consist of three main categories. These are rational decision-making, cognitive misperceptions, and psychological commitment. The first, rational decision-making implies an assessment of relative costs and benefits of change i.e. using a technology such as MIDAS to support health policy decision-making. In the case where the costs of using the new technology appear to outweigh the benefits, status quo bias will result. These costs may comprise both transition costs (such as learning costs associated with using the new technology) and uncertainty costs which represent the perception of risk or psychological uncertainty associated with adoption of the new technology. Clearly switching to a new system that can support decision-making may increase the uncertainty costs for those users who are unsure about the impact of this change. The second factor with potential to result in status quo bias in relation to MIDAS is cognitive misperceptions. Within this category, the impact of loss aversion cannot be underestimated. The literature had adequately demonstrated the impact of psychological principles such as loss aversion in relation to human decision-making (Kahneman and Tversky 1979), showing that losses carry greater psychological weighting than gains in value perception. Loss aversion therefore has potential to influence status quo bias in relation to adoption of MIDAS because the evaluation of perceived loss in changing from current decision-making methods may carry greater impact than any perceived gain from using the system. The final factor with potential to influence status quo bias is psychological commitment, which involves factors that are more nuanced and therefore more difficult to influence. For example, psychological commitment can be influenced by sunk costs, social norms and efforts to feel in control. The sunk costs such as previous time, effort and comfort associated with a particular way of doing things have potential to increase switching costs and reluctance to change to an alternative solution. Social norms will influence adoption of a technology through colleagues’ opinions about whether they think it is a good idea to use the new technology. Efforts to feel in control relate to the desire to direct a situation. This could manifest in resistance to MIDAS due to a desire for specific outcomes that the system may not necessarily support, or not support to the level required to justify them adequately.
Consideration of these issues relating to status quo bias is particularly relevant for the success of MIDAS and its impact. However, as status quo bias is a subconscious phenomenon, it is unlikely that the insights necessary to determine the degree to which individual factors are at play and to interpret them correctly can be obtained via survey or more conventional data collection and interpretation methodologies. In fact, information systems researchers, behavioural researchers and health economists are realizing that to ensure optimal adoption and usage outcomes from innovative technology and systems requires a far more nuanced examination and interpretation than has been the case to date. It is clear that the use of qualitative methods of exploration and description alongside mainstream quantitative techniques contains great potential to understand the impact of MIDAS. Consequently the DCU MIDAS research team are employing Q methodology (in addition to several series of interviews) in order to understanding what factors influence adoption and impact of MIDAS as it provides a means of exploring user subjectivity, beliefs and values, whilst at the same time preserving the transparency, rigour and mathematical underpinnings that tend to be considered the preserve of mainstream quantitative techniques.
The process employed by the DCU team consists of four step-wise stages: selecting the Q set (which are items to be sorted), selecting a sample of individuals (known as the P set), the Q sorting process (where respondents rank items), followed by a ‘by-person’ factor analysis and the interpretation of factors. The DCU researchers conducted preliminary interviews with health policy decision makers who are participating in the MIDAS project. On the basis of those interviews, a Q set was constructed to respondents (health policy decision makers) who then reflect on their views, beliefs and perceptions in relation to specific statements. The respondents then rank these items in relation to each other and the subsequent factor analysis reveals a small number of underlying perspectives. It is the interpretation of those factors that enables development of a structure that identifies commonalities and correlations. Furthermore, it reveals a deeper understanding of issues which most influence respondents in their thinking, thereby identifying what factors need to be addressed to ensure the successful adoption and maximal impact of MIDAS.
Researcher bias is minimized as it allows participants’ voices to be heard and it does not simply elicit opinions regarding the attributes of MIDAS, but also provide information on the individual’s intensity of preference within a specific economic and policy context, while recognizing the opportunity costs associated with MIDAS. In line with qualitative approaches, the a priori constructs are not imposed on the respondent by researchers but derive from open interviews. Subjective opinions, beliefs and values are elicited and a relatively small sample is used to explore the rich diversity of accounts in relation to MIDAS. Where this approach differs from qualitative methods, however, is in the means of data collection and analysis as Q uses the mathematical approach of factor analysis to identify underlying patterns in data.
This approach is particularly valuable from an impact analysis perspective as it enables preference elicitation; the inclusion of local contextual factors into the impact evaluation and case study design; and the generation and development of behavioural models. Therefore it provides significant opportunity to examine the underlying factors that need to be addressed in order to diminish status quo bias and ensure maximal adoption and continued usage of MIDAS.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Samuelson, W., & Zeckhauser, R. J. (1988). Status Quo Bias in Decision Making. Journal of Risk &Uncertainty, 1, 7-59.