The timely adoption of technology for organizations in making the right investment or divestment can be achieved by using multicriteria decision making approach with integrated views of established innovation theories, industry best practices in technology acquisition lifecycle, statistical analysis of available technology profiles, expert opinion and trend analysis. This research aimed to develop an analytical approach to assess the correlation among objective data (such as innovation maturity rating and market penetration) and subjective data (such as benefit rating and “time to plateau”) to provide organizations insights in technology adoption decisions. The objective of this study is not to study the Gartner’s Hype Cycles but to utilize the longitudinal technology innovation profile data as factors for informed technology adoption decision. We combined mapping with Department of Defense Technology Readiness Level, statistical analysis, correlations, multiple regression analysis and trend analysis to provides an objective and quantifiable methodology to provide insight into the characteristics of innovations. The goal is to derive a logical and balanced approach for organizations’ decision-making base on objective (as in the technology maturity rating and market survey) and subjective (as in the expert opinion in benefit rating and time to plateau predictions) data analysis. We used Rogers’ concept of “Diffusion of Innovation” as a notional reference for Organizational Technology Adoption to conduct a statistical analysis of a selected set of 345 Gartner’s technology profile data from 2009 to 2015. We used market penetration data as a proxy for technology acceptance. To ensure the fit for purpose, we compared Gartner’s definition of technology maturity with that of the Department of Defense Technology Readiness Level (TRL). The trending data on market penetration, maturity rating, benefit rating and time to technology plateau determined that the 2nd Order Polynomial Model provided the best statistical goodness of fit in all cases. We discuss the non-linear nature of the data and the for more predictive association of technological maturity with organizational adoption. Further empirical approaches with traditional analysis, machine learning or artificial intelligence would allow researchers to test, to explore and to better understand the diffusion of innovation first pioneered by Rogers, Moore and Bass.
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