Technology Diffusion: An Empirical Test of Competitive Effects

1989 ◽  
Vol 53 (1) ◽  
pp. 35-49 ◽  
Author(s):  
Hubert Gatignon ◽  
Thomas S. Robertson
Keyword(s):  
Information Processing ◽  
Empirical Study ◽  
Technology Diffusion ◽  
Technology Innovation ◽  
Empirical Test ◽  
High Technology ◽  
Technological Innovations ◽  
Competitive Effects

The authors provide an empirical test of the effects of competition on the adoption of technological innovations by organizations. They follow the conceptualization developed in the model they proposed previously in the Journal of Marketing. An empirical study of the factors accounting for the adoption or rejection of a high technology innovation is reported. The results suggest that firms most receptive to innovation are in concentrated industries with limited price intensity and that supplier incentives and vertical links to buyers are important in achieving adoption. The results also suggest that adopters can be separated from nonadopters by their information-processing characteristics.

Technology Diffusion: An Empirical Test of Competitive Effects

1989 ◽  
Vol 53 (1) ◽  
pp. 35 ◽  
Author(s):  
Hubert Gatignon ◽  
Thomas S. Robertson
Keyword(s):  
Technology Diffusion ◽  
Empirical Test ◽  
Competitive Effects

Competitive Effects on Technology Diffusion

1986 ◽  
Vol 50 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Thomas S. Robertson ◽  
Hubert Gatignon
Keyword(s):  
Technology Diffusion ◽  
New Technologies ◽  
High Technology ◽  
Competitive Environment ◽  
Supply Side ◽  
Business Organizations ◽  
Competitive Effects ◽  
Explanatory Variables ◽  
Factors Influencing ◽  
Behavioral Paradigm

This article takes as its central concern the diffusion of high technology innovation among business organizations. A set of propositions is developed that focuses on the competitive factors influencing diffusion. The article suggests how the supply-side competitive environment and the adopter industry competitive environment both affect diffusion of new technologies. The article seeks to extend the current behavioral paradigm for studying innovation diffusion by incorporating competitive factors as explanatory variables.

scholarly journals AN ANALYSIS OF THE DETERMINANTS OF PADDY AND PULSE DEVELOPMENT IN PAPUA PROVINCE

2016 ◽  
Vol 12 (2) ◽  
pp. 111
Author(s):  
Demas Wamaerand ◽  
Kuntoro Boga Andri
Keyword(s):  
Sweet Potato ◽  
Success Factors ◽  
Technology Innovation ◽  
Distribution Patterns ◽  
Agricultural Technology ◽  
Technological Innovations ◽  
Sweet Potatoes ◽  
Seed Sources ◽  
Research Activities ◽  
Soybean Plants

This study aims to: (1) mapping the distribution pattern of the application of agricultural technology innovation specific locations, (2) determine the critical success factors  distribution  application  of  agricultural  technology  innovation  specific locations,  (3)  improvement  of  distribution  patterns  and  acceleration  of  the  adoption and diffusion of technological innovations to support agribusiness and agroindustrial rice, sweet potatoes and  soybeans in Papua. The research activities carried out during March  2011  to  February  2014  using  three  approaches  (methods),  namely  (1)  Desk Study  on  the  SL-  PTT  rice,  soybean  and  sweet  potato  (2)  surveys  to  obtain quantitative  data in  three  districts  purposively  selected  with  10-  20  respondents, (3) the application pattern of diffusion through the demonstration of quality seeds of rice, corn  and  soybeans  in  the  BPP  or  a  farmer  seed  sources  in  three  selected  districts.Agronomic  data  were tabulated  and  analyzed  descriptively.  Analysis  of  the level  of efficiency  in  the  application  of  technology  used  indicator  plots  the  balance  receiptsand  fees  or  analysis  of  R  /  C  ratio.  To  measure  the  success  of  the  application  of technological  innovations  in  the  plots  need  to  set  performance  indicators,  covering aspects of the use of inputs, processes, outputs, outcomes, benefits and impacts. The results show that the dissemination of technological innovations for the development of location-specific agricultural commodities  of rice, corn, soybean and sweet potato, has  spread  in  most  regions  crop  farming  development  centers  in  Papua.  But  only concentrated  around  the  transmigration  settlement  area.  New  varieties  of  soybean plants  yielding  seeds  and  rice  showed  better  productivity  than  the  old  varieties  that have  been  repeatedly  planted  by  farmers.  Yielding  varieties  of  maize  is  being introduced less developed because it is still constrained by marketing, if the market is readily available, farmers are willing to develop it.

A Theoretical Technology Adoption Rate Model

2011 ◽  
pp. 321-331
Author(s):  
Jeanne C. Samuel
Keyword(s):  
Technology Adoption ◽  
Technology Diffusion ◽  
Technology Innovation ◽  
Innovation Adoption ◽  
Rate Model ◽  
Hypothetical Model ◽  
Adoption Rate ◽  
Hype Cycle ◽  
J Curve ◽  
S Curve

This article proposes a hypothetical model for determining rate of diffusion of an innovation in a system. The model modifies Everett Rogers’ S-curve using an index created from Gartner’s hype cycle phases. Rogers’ model for technology innovation adoption demonstrates that cumulative technology diffusion in a system from zero through the late majority adopters’ phase forms a curve resembling the letter “S”. Hype cycles analyze the five emotional stages technology adopters go through from over-enthusiasm (hype) though disappointment until it plateaus (beginning of mainstream adoption). When numbers assigned to the phases of adoption from the hype cycle are used as multipliers and applied to the cumulative adoption data of an innovation (Rogers’ S-curve), the “S” becomes a “J”. With the J-curve you can determine the rate of innovation diffusion in an organization.

Evaluation of Technological Innovations and the Industrial Ecosystem of Science Parks in Shanghai: An Empirical Study

2020 ◽  
Vol 25 (3) ◽  
pp. 482-504
Author(s):  
Min-Ren Yan ◽  
Haiyan Yan ◽  
Lingyun Zhan ◽  
Xinyue Yan ◽  
Mengen Xu
Keyword(s):  
Empirical Study ◽  
Driving Forces ◽  
Policy Implications ◽  
Technological Innovations ◽  
Science Parks ◽  
Loop Analysis ◽  
Innovation Ecosystem ◽  
Science Park ◽  
Innovation Activities ◽  
The Government

Science parks and innovation policies have a major mission in driving innovative resources and nurturing emerging industries, while the government-academia-industry collaborations and the establishment of an ecosystem are essentials. To investigate the key driving forces for sustainable development of the collaborative ecosystem, this article evaluates the technological innovations and the ecosystem of Science Parks in Shanghai based on historical data obtained from Shanghai Zhangjiang Science Park (Zhangjiang Park in short). Systems thinking and causal loop analysis are adopted to explore the structure of the collaborative ecosystem and reflections of the policy impact on the science park. The role of the government in science parks and innovation ecosystems is identified with systems mapping and empirical study. The economic impact of Zhangjiang Park policies and the performance of innovation activities in Shanghai are further evaluated. Lessons learnt from the benchmarked science parks and policy implications for facilitating the innovation ecosystem are addressed.

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