Work

Exploring How Acquired Diversity of Teams Influences the Innovation Diffusion in Artificial Intelligence

Year: 2024

Type: article

Abstract: ABSTRACT This study explores innovation diffusion in Artificial Intelligence (AI) over the past 70 years, focusing on the impact of acquired diversity(including discipline diversity and topic diversity) of teams. We obtained 906,065 AI papers with 18,357,761 citations written by 1,316,936 authors between 1950 and 2015 from the Microsoft Academic Graph. Using citation analysis, we examine how AI innovations spread and the influence of the acquired diversity of teams on this process. We measure innovation diffusion from four aspects: innovation speed, innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity. Our findings show that: (1) AI papers published before and after 2000 demonstrate distinct diffusion patterns. AI papers published before 2000 show a bimodal citation trend, while those released after 2000 present only a single peak in citations. (2) The first citation interval can be reduced by 2.35 years before 2000 and by 0.27 years after 2000 by enhancing topic diversity. (3) Both discipline diversity and topic diversity effectively promote innovation diffusion power, innovation diffusion breadth, and innovation diffusion breadth diversity in the field of AI.

Source: Proceedings of the Association for Information Science and Technology

DOI: 10.1002/pra2.1075

Authors: Xuli Tang, Xin Li, Ming Yi

Institutions: Central China Normal University, Huazhong University of Science and Technology

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Central China Normal University, Fundamental Research Funds for the Central Universities

Awards: G5543326775, G8310455107