scholarly journals Macro Patterns and Trends of U.S. Consumer Technological Innovation Diffusion Rates

Systems ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Albert Joseph Parvin ◽  
Mario G. Beruvides
Keyword(s):  
Innovation Diffusion ◽  
Technological Innovation ◽  
Research Effort ◽  
Decision Makers ◽  
Macro Level ◽  
Technological Innovations ◽  
Exploratory Data ◽  
Diffusion Rates ◽  
Technological Innovation Diffusion ◽  
Aid Decision

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.

scholarly journals Optimizing the Abandonment of a Technological Innovation

Systems ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 27
Author(s):  
Albert Joseph Parvin ◽  
Mario G. Beruvides
Keyword(s):  
Technological Innovation ◽  
Exploratory Data Analysis ◽  
Diffusion Rate ◽  
Primary Objective ◽  
Decision Makers ◽  
Stochastic Simulations ◽  
Macro Level ◽  
Assessment Framework ◽  
Exploratory Data ◽  
The Impact

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.

Firm heterogeneity and innovation diffusion performance: absorptive capacities

2019 ◽  
Vol 58 (4) ◽  
pp. 725-742 ◽  
Author(s):  
Hongying Wang ◽  
Bing Sun
Keyword(s):  
Innovation Diffusion ◽  
Technological Innovation ◽  
Absorptive Capacity ◽  
Technology Diffusion ◽  
Structural Equation ◽  
Firm Heterogeneity ◽  
Content Type ◽  
Technological Innovation Diffusion ◽  
The Impact ◽  
Positive Effect

Purpose The purpose of this paper is to undertake research on the relationship of firm heterogeneity and innovation diffusion performance, and the role of absorptive capacity in this relationship. Design/methodology/approach Based on the diffusion of innovation theory, enterprise heterogeneity directly affects the evaluation stage (considering whether to adopt it) and the experimental stage (observing whether it is suitable for one’s own situation) of the diffusion process. Therefore, the paper uses a structural equation model to construct the influencing factors model of enterprise heterogeneity on technology diffusion. Furthermore, questionnaires were distributed to 236 enterprises with different scales, nature and location to explore the impact of heterogeneity on technology diffusion with scientific, objective and comprehensive data. Findings Firm heterogeneity has a positive effect on absorptive capacity and absorptive capacity has a positive effect on technological innovation diffusion performance. Thus, absorptive capacity plays an intermediary role in the effect on enterprise heterogeneity and technological innovation diffusion performance. More interestingly, the authors get some results that are not entirely consistent with the theoretical assumptions. Practical implications Firm heterogeneity plays a central role in the process of innovation diffusion. Enterprises should build internal management platforms to enhance cooperation among employees, and establish links with other enterprises for opportunities for win-win cooperation. In addition, enterprises should control the frequency of internal activities, which will undermine the enthusiasm of enterprise members to participate in technology sharing. Originality/value This paper explores the interaction between technology potential, cooperation frequency and absorptive capacity from the perspective of systems theory. The findings enrich the theory of innovation diffusion, and explore the inherent reasons why enterprise heterogeneity affects innovation diffusion. Furthermore, the theory that intra-firm cooperation promotes innovation diffusion is not always correct.

scholarly journals Business models for maximising the diffusion of technological innovations for climate-smart agriculture

2017 ◽  
Vol 20 (1) ◽  
pp. 5-23 ◽  
Author(s):  
Thomas B. Long ◽  
Vincent Blok ◽  
Kim Poldner
Keyword(s):  
Technological Innovation ◽  
Business Models ◽  
Customer Relationships ◽  
Limited Attention ◽  
Technological Innovations ◽  
Critical Issues ◽  
Adoption And Diffusion ◽  
Smart Agriculture ◽  
Technological Innovation Diffusion ◽  
Key Resources

Technological innovations will play a prominent role in the transition to climate-smart agriculture (CSA). However, CSA technological innovation diffusion is subject to socio-economic barriers. The success of innovations is partly dependent on the business models that are used to diffuse them. Within the context of innovations for CSA, the role that innovation providers’ business models play in the successful adoption and diffusion has received limited attention. In this paper we identify critical issues for business models for CSA technological innovations (BMfCSATI). Our results indicate that current BMfCSATIs are not optimised for diffusing CSA technological innovations. Critical business model elements include the value proposition, channels, customer relationships, key resources, key partners, and cost structure. We find a disparity between the views of CSA technological innovation providers and potential users. The paper explores the implications of the results and develops recommendations for CSA technological innovation providers’ business models.

scholarly journals Interpreting, analyzing and distributing information: A big data framework for competitive intelligence

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Eduardo Luis Casarotto ◽  
Guilherme Cunha Malafaia ◽  
Marta Pagán Martínez ◽  
Erlaine Binotto
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Technological Innovation ◽  
New Technologies ◽  
Decision Makers ◽  
Strategic Decision ◽  
Competitive Intelligence ◽  
Technological Innovations ◽  
Data Sets ◽  
Data Framework

This paper aimed to develop a data-based technological innovation frameworkfocused on the competitive intelligence process. Technological innovations increasinglytransform the behavior of societies, affecting all sectors. Solutions such as cloud computing, theInternet of Things, and artificial intelligence provide and benefit from a vast generation of data:large data sets called Big Data. The use of new technologies in all sectors increases in the faceof such innovation and technological mechanisms of management. We advocated that the use ofBig Data and the competitive intelligence process could help generate or maintain a competitiveadvantage for organizations. We based the proposition of our framework on the concepts of BigData and competitive intelligence. Our proposal is a theoretical framework for use in thecollection, treatment, and distribution of information directed to strategic decision-makers. Itssystematized architecture allows the integration of processes that generate information fordecision making.

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