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BMIS-IoT 2019 - Workshop on Business Model Innovation and Sustainability in IoT (BMIS-IoT)

Date2019-06-17 - 2019-06-21

Deadline2019-01-18

VenueAarhus, Denmark Denmark

Keywords

Websitehttps://iotweek.org

Topics/Call fo Papers

The interdisciplinary studies of interest include but are not limited to:
1. Case studies and longitudinal studies of BMI in IoT: Position and technical papers describing the usage of IoT and BMI methods used for
developing new businesses ideas and related innovation processes. This also includes the driving stakeholders (e.g., developers, industry, endusers, standardization organs) influenced by targeted market and audience, business development, education, legislation, ethics, development
cycles, and theory.
2. Evaluation of IoT application and innovation in terms of business potential toward sustainability: With focus on the IoT technology in the
business models both in the innovation and the development stages and on business models in the market. Evaluation or IoT applications and
services in terms of privacy and security towards trusted and sustainable deployments are of particular interest.
3. Economic, social, and ecological aspects for IoT sustainability: With focus on IoT platforms and frameworks that improve life and interact
with the environment having consequences with a high impact on existing solutions, technologies, and businesses. Position papers on multistakeholder platforms, where centralization is challenged and models such as cooperative networks and platforms are encouraged.
4. IoT technology influences and cross-sectoral learnings on adoption of IoT technology: Studies describing IoT technology utilized in
different ways across different sectors. Also, studies describing the influence of deployed IoT technology in the same sectors or business models.
Setting-up new interactions in existing ecosystems and the emergence of new ecosystems upon the deployment of IoT technology. Adoption of IoT
and data driven business models moving across industry sectors showing significant learning. The use of cases and real implementations as well
as studies giving evidence of performance and accuracy of solutions.

Last modified: 2018-12-29 21:40:49