Macro examination of technological innovation diffusion rates and abandonment assessment



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Macro-level trends and patterns are commonly used in business, science, finance, and engineering to provide insights and estimates to help decision-makers make informed decisions. This research effort explored and developed macro-based perspective knowledge to provide decision-makers with general system-level intelligence towards assessing technological innovation abandonment. The research drives is to create value through knowledge gained via the macro-perspective exploration of factors to improve abandonment assessment, to enable abandonment optimization, to enable proactive abandonment decisions, and to reduce abandonment assessment complexity.

Specifically, the overall endeavor strives to create and support macro-based knowledge to address: what is the optimal point of abandonment of a technological innovation, and what is the economic impact of the speed at which the abandonment decision is made? This effort explores factors and relational assessment areas common to each in a series of three research paper explorations to build and develop macro-based technological innovation abandonment intelligence. The factors and relational areas explored are the diffusion rates of technological innovation, the optimization of the abandonment event, and the quantification of how accurately a technological innovation’s diffusion rate can be forecasted with partial diffusion data. This research effort followed a three research paper format. Abstracts of each research paper are:

Paper #1: Macro Patterns and Trends of U.S. Consumer Technological Innovation Diffusion Rates Macro-level trends and patterns are commonly used in business, science, finance, and engineering to provide insights and estimates to assist decision-makers. In this research effort, macro-level trends and patterns were explored on the diffusion rates of technological innovations, a component of a sorely under-studied question in technology assessment: When should a technological innovation be abandoned? A quantitative exploratory data analysis (EDA) based approach was employed to examine diffusion market data of 42 U.S. consumer technological innovations from the early 1900s to the 2010s to extract general macro-level knowledge on technological innovation diffusion rates. A goal of this effort is to grow diffusion rate knowledge to enable the development of general macro-based forecasting tools. Such tools would aid decision-makers in making informed and proactive decisions on when to abandon a technological innovation. This research offers several significant contributions to the macro-level understanding of the boundaries and likelihood of achieving a range of technological innovation diffusion rates. These contributions include the determination that the frequency of diffusion rates are positively skewed when ordered from slowest to fastest, and the identification and ranking of probability density functions that best represent the rates of technological innovation diffusion.

Paper #2: Optimizing the Abandonment of a Technological Innovation The primary objective of this study is to reveal macro-level knowledge to aid the optimization, evaluation, and strategic planning of technological innovation abandonment. This research uses an exploratory data analysis (EDA) approach to extract directional and associative patterns (macro-level knowledge) to assess technological innovation abandonment optimization. Deterministic and stochastic simulations are employed to reveal the impact of three factors on abandonment optimization, namely, a technological innovation’s diffusion rate, a technological innovation’s probability of achieving a given diffusion rate, and the point of abandonment. The patterns and insights revealed through the graphical examination of the simulation provide associative and directional knowledge to assess the abandonment optimization of technological innovation. These revealed patterns and insights enable decision-makers to develop an abandonment assessment framework for optimizing, evaluating, and proactively planning abandonment at the macro level.

Paper #3: In-Situ Technological Innovation Diffusion Rate Accuracy Assessment This research identifies underlying macro-level trends of diffusion rate assessment using historical technological innovation diffusion data to explore the statistical characteristics of diffusion rate percent-error of the Bass and logistic model; time stepped through its lifecycle. A quantitative exploratory data analysis (EDA) based approach was employed to uncover underlying macro-perspective patterns and insights on a technological innovation’s forecasted diffusion rate percent-error using the data of 42 matured U.S. consumer technological innovations. An objective of this effort is to determine the statistical characteristics (mean, median, variance, standard deviation, skewness, and kurtosis) of diffusion rate assessment using the Bass and logistic model at various points in a technological innovation’s lifecycle to reveal underlying directional and associative insights. Developing such insights and a framework for accessing in-situ (real-time) a technological innovation’s diffusion rate percent-error would benefit an organization’s decision-makers in maximizing gains and minimizing losses. These insights include identifying whether the Bass and logistic models are more likely to overestimate or underestimate a technological innovation’s diffusion rate when assessed at various points in its diffusion life cycle. Practitioners can use such information to set resource investment strategies and policies based on risk tolerance and the utility of the weighted outcomes via decision theory tools.

Embargo status: Restricted until 01/2023. To request the author grant access, click on the PDF link to the left.



Technological Innovation, Abandonment, Diffusion Rate, Macro Assessment