Internet of things technology diffusion forecasts

Yorgos D. Marinakis, Steven T. Walsh, Rainer Harms

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.

Original languageEnglish
Title of host publicationPICMET 2017 - Portland International Conference on Management of Engineering and Technology
Subtitle of host publicationTechnology Management for the Interconnected World, Proceedings
EditorsTimothy R. Anderson, Kiyoshi Niwa, Dundar F. Kocaoglu, Tugrul U. Daim, Dilek Cetindamar Kozanoglu, Gary Perman, Harm-Jan Steenhuis
PublisherIEEE
Number of pages5
Volume2017-January
ISBN (Electronic)9781890843366
DOIs
Publication statusPublished - 29 Nov 2017
EventPortland International Conference on Management of Engineering and Technology 2017: Technology Management for Interconnected World - Portland Marriott Downtown Waterfront, Portland, United States
Duration: 9 Jul 201713 Jul 2017

Conference

ConferencePortland International Conference on Management of Engineering and Technology 2017
Abbreviated titlePICMET 2017
CountryUnited States
CityPortland
Period9/07/1713/07/17

Fingerprint

infrastructure
Internet
Sensors
Explosions
Internet of things
Technology diffusion
Sensor
lack
Wireless networks
Availability

Cite this

Marinakis, Y. D., Walsh, S. T., & Harms, R. (2017). Internet of things technology diffusion forecasts. In T. R. Anderson, K. Niwa, D. F. Kocaoglu, T. U. Daim, D. C. Kozanoglu, G. Perman, & H-J. Steenhuis (Eds.), PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings (Vol. 2017-January). IEEE. https://doi.org/10.23919/PICMET.2017.8125435
Marinakis, Yorgos D. ; Walsh, Steven T. ; Harms, Rainer. / Internet of things technology diffusion forecasts. PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. editor / Timothy R. Anderson ; Kiyoshi Niwa ; Dundar F. Kocaoglu ; Tugrul U. Daim ; Dilek Cetindamar Kozanoglu ; Gary Perman ; Harm-Jan Steenhuis. Vol. 2017-January IEEE, 2017.
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Marinakis, YD, Walsh, ST & Harms, R 2017, Internet of things technology diffusion forecasts. in TR Anderson, K Niwa, DF Kocaoglu, TU Daim, DC Kozanoglu, G Perman & H-J Steenhuis (eds), PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. vol. 2017-January, IEEE, Portland International Conference on Management of Engineering and Technology 2017, Portland, United States, 9/07/17. https://doi.org/10.23919/PICMET.2017.8125435

Internet of things technology diffusion forecasts. / Marinakis, Yorgos D.; Walsh, Steven T.; Harms, Rainer.

PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. ed. / Timothy R. Anderson; Kiyoshi Niwa; Dundar F. Kocaoglu; Tugrul U. Daim; Dilek Cetindamar Kozanoglu; Gary Perman; Harm-Jan Steenhuis. Vol. 2017-January IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Walsh, Steven T.

AU - Harms, Rainer

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N2 - Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.

AB - Prognosticators and pundits are forecasting an explosion over the next decade in the number of sensors connected to wired and wireless networks, also referred to as the Internet of Things. The challenge is that these sensor forecasts are being made without taking into account the infrastructure required to manufacture and operate the sensors. Financial forecasts of individual infrastructure components have been made, but they give point forecasts rather than diffusion curves. It is also often not clear what models these forecasters are using, as they are often in proprietary reports. The present study provides sensor and sensor infrastructure technology component diffusion forecasts using a sigmoidal model of product diffusion. A plurality of technology diffusion curves was computed, one for each sensor infrastructure component technology. To identify the potential lack of availability of a component or a set of components, the forecast curves were then examined for temporal commonalities and differences. Thus this study provides a method for forecasting an emerging technology.

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Marinakis YD, Walsh ST, Harms R. Internet of things technology diffusion forecasts. In Anderson TR, Niwa K, Kocaoglu DF, Daim TU, Kozanoglu DC, Perman G, Steenhuis H-J, editors, PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings. Vol. 2017-January. IEEE. 2017 https://doi.org/10.23919/PICMET.2017.8125435