Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles

Li, Shuying; Garces, Edwin; Daim, Tuğrul U.

doi:10.1016/j.techfore.2019.119731

Cited by 55 publications

(16 citation statements)

References 41 publications

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“…The process ends once it either reaches a certain number of rounds or achieves a steady consensus; the answers from the final round determine the result. These manual methods often require a large amount of contribution from numerous field-related experts and hence are expensive to utilize, but still have been used in recent years [5] for its high domain adaptability.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJWesT

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Technological developments are not isolated and are influenced not only by similar technologies but also by many entities, which are sometimes unforeseen by the experts in the field. The authors propose a method for identifying technology-relevant entities with trend curve analysis. The method first utilizes the tangential connection between terms in the encyclopedic dataset to extract technology-related entities with varying relation distances. Changes in their term frequencies within 389 million academic articles and 60 billion web pages are then analyzed to identify technology-relevant entities, incorporating the degrees and changes in both academic interests and public recognitions. The analysis is performed to find entities both significant and relevant to the technology of interest, resulting in the discovery of 40 and 39 technologyrelevant entities, respectively, for unmanned aerial vehicle and hyperspectral imaging with 0.875 and 0.5385 accuracies. The case study showed the proposed method can capture hidden relationships between seemingly unrelated entities.

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Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJWesT

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“…Numerous methods had been proposed to reveal information hidden in the technology data [12][13][14][15][16][17][18][19]. Generally, technology forecasting methods can be divided into two categories depending on the degree of dependence on data: qualitative methods and quantitative methods [12].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it is more suitable for the early stage of a certain technology. With the advancement of computer technology and related numerical tools, more and more attention had been paid to quantitative methods [14][15][16][17][18][19]. Statistical tools [14][15][16] and text mining tools [17][18][19] are widely used in technology forecasting, which can deal with massive raw data.…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of computer technology and related numerical tools, more and more attention had been paid to quantitative methods [14][15][16][17][18][19]. Statistical tools [14][15][16] and text mining tools [17][18][19] are widely used in technology forecasting, which can deal with massive raw data. As text mining is able to find implicit, previously unknown, and potentially useful patterns from large text documents [20], it becomes the mainstream method of technology forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…e quality of results of forecasting is deeply influenced by the type and quality of the documents. For a long time, patents were recognized as a crucial data source for the study of technology innovation [15] and technology forecasting [21,22]. However, patents are not enough to get a complete picture of technology developments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting the Development of Self-Driving Technology in China by Multidimensional Information

Zhou

et al. 2021

Journal of Advanced Transportation

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Due to the demand for safety and convenience in traveling, self-driving technology has developed very fast in the past decades. In this paper, a novel technology forecasting model is developed. The topic-based text mining and expert judgment approaches are combined to forecast the technology trends efficiently and accurately. To improve the reliability of the results, multidimensional information including scientific papers, patents, and industry data is considered. Then, the model is utilized to forecast the development trends of self-driving technology in China. Data ranging from 2002 to 2019 are adopted with proper data cleaning. Topic clustering for papers and patents is performed, and the hierarchical structures are constructed. On this basis, the results of technology’s evolution based on papers and patents are compared and the development trends are obtained. With these results, it is speculated that technology on “Decision” will be the next hotspot in patents. The research results of this paper will provide reference and guidance for Chinese enterprises and government in decision-making on self-driving technology.

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