Data-driven network visualization for innovation and competitive intelligence

Serhad Sarica; Bowen Yan; Gerardo Bulato; Pratik Jaipurkar; Jianxi Luo

doi:10.24251/hicss.2019.017

Data-driven network visualization for innovation and competitive intelligence

Serhad Sarica, Bowen Yan, Gerardo Bulato, Pratik Jaipurkar, Jianxi Luo

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

4 Downloads (CityUHK Scholars)

Abstract

Technology positions of a firm may determine its competitive advantages and innovation opportunities. While a tangible understanding of the technology positions of a firm, i.e., the set of technologies the firm has mastered, can inform innovation and competitive intelligence, yet such positions are heterogeneous, intangible and difficult to analyze. Herein, we present a data-driven network visualization methodology to locate the knowledge positions of a firm as a subspace of the total technology space for innovation and competitive intelligence analytics. The total technology space is empirically constructed as a network map of all patent technology classes and can be overlaid with the knowledge positions of a firm according to its patent records. This paper demonstrates how to use the system to conduct historical, comparative and predictive analyses of the technology positions of individual and different firms. The methodology has been implemented into a cloud-based data-driven visual analytics system - InnoGPS. © 2019 IEEE Computer Society. All rights reserved.

Original language	English
Title of host publication	Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
Publisher	IEEE Computer Society
Pages	127-135
Volume	2019-January
ISBN (Print)	9780998133126
DOIs	https://doi.org/10.24251/hicss.2019.017
Publication status	Published - Jan 2019
Externally published	Yes
Event	52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States Duration: 8 Jan 2019 → 11 Jan 2019 https://scholarspace.manoa.hawaii.edu/handle/10125/59440

Publication series

Name	Proceedings of the Annual Hawaii International Conference on System Sciences
Volume	2019-January
ISSN (Print)	1530-1605

Conference

Conference	52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
Place	United States
City	Maui
Period	8/01/19 → 11/01/19
Internet address	https://scholarspace.manoa.hawaii.edu/handle/10125/59440

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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10.24251/hicss.2019.017

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Sarica, S., Yan, B., Bulato, G., Jaipurkar, P., & Luo, J. (2019). Data-driven network visualization for innovation and competitive intelligence. In Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 (Vol. 2019-January, pp. 127-135). (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 2019-January). IEEE Computer Society. https://doi.org/10.24251/hicss.2019.017

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abstract = "Technology positions of a firm may determine its competitive advantages and innovation opportunities. While a tangible understanding of the technology positions of a firm, i.e., the set of technologies the firm has mastered, can inform innovation and competitive intelligence, yet such positions are heterogeneous, intangible and difficult to analyze. Herein, we present a data-driven network visualization methodology to locate the knowledge positions of a firm as a subspace of the total technology space for innovation and competitive intelligence analytics. The total technology space is empirically constructed as a network map of all patent technology classes and can be overlaid with the knowledge positions of a firm according to its patent records. This paper demonstrates how to use the system to conduct historical, comparative and predictive analyses of the technology positions of individual and different firms. The methodology has been implemented into a cloud-based data-driven visual analytics system - InnoGPS. {\textcopyright} 2019 IEEE Computer Society. All rights reserved.",

author = "Serhad Sarica and Bowen Yan and Gerardo Bulato and Pratik Jaipurkar and Jianxi Luo",

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Sarica, S, Yan, B, Bulato, G, Jaipurkar, P & Luo, J 2019, Data-driven network visualization for innovation and competitive intelligence. in Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019. vol. 2019-January, Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2019-January, IEEE Computer Society, pp. 127-135, 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019, Maui, United States, 8/01/19. https://doi.org/10.24251/hicss.2019.017

Data-driven network visualization for innovation and competitive intelligence. / Sarica, Serhad; Yan, Bowen; Bulato, Gerardo et al.
Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019. Vol. 2019-January IEEE Computer Society, 2019. p. 127-135 (Proceedings of the Annual Hawaii International Conference on System Sciences; Vol. 2019-January).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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ER -

Sarica S, Yan B, Bulato G, Jaipurkar P, Luo J. Data-driven network visualization for innovation and competitive intelligence. In Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019. Vol. 2019-January. IEEE Computer Society. 2019. p. 127-135. (Proceedings of the Annual Hawaii International Conference on System Sciences). doi: 10.24251/hicss.2019.017

Data-driven network visualization for innovation and competitive intelligence

Abstract

Publication series

Conference

Bibliographical note

Publisher's Copyright Statement

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