Skip to main content

Advertisement

SpringerLink
Log in

Digitizing Human Behavior with Wireless Sensors in Biogas 2020 Technological Business Model Innovation Challenges

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The ability to develop innovative Business Models (BMs) with high speed and good Return of Investment has become the cornerstone for the competitiveness of many successful businesses operating in many different Business Model Ecosystems (BMES) today. Business Model Innovation (BMI) appears even more important in these days—as well as in the Biogas 2020 BMES—as digitalization affect all areas of innovating and operating businesses. Increased digitalization leads to new ways of working, communicating, cooperating and supporting BMI. Thanks to these new technologies todays businesses are more interconnected in physical, digital and virtual networks. The cooperation between businesses, academia and the use of competences across different domains becomes more important to strengthen BMI and advance BMI. One main challenge however of digitization of BMI is modeling human behavior and cognitive processes in BMI processes into logical expressions that can be digitized and automated. Our state-of-the-art review shows that has been no research in this field related to BMI. The aim is therefore to find a way to value the BMI process and support the BMI process in a more advanced way than previously supported by sensor technologies. This paper report on a first experiment carried out in a cross interdisciplinary CGC research group with 3 different European Biogas Technological BMI (TBMI) challenges. The aim of this first experiment was twofold. (1) To support the test and further development of a conceptual model of a future agile BMI learning environment first presented at the Global Wireless Summit 2017 conference in Cape Town, South Africa. The the aim is later to adapt these results, findings and experiment recommendation to prepare a largescale global experiment in 2019. The used sensor technology backbone was to this experiment made as a special build cloud-based sensing BM and sensing BMI room—imbedded in special built BMI rooms called B-lab/Beecube. The B-labs was embedded with advanced mobile and wireless sensors, both environmental and wearable by the participants. The aim was to test use and utilization of these sensors in BMI to improve observations, improve analyzing abilities and predict in a latter stage human behavior in BMI processes, (2) to support the development of an advanced network based BMI environment and in a latter perspective to support a Multi Business Model Innovation (MBMI) framework embedded with AI, machine learning, AI, Multi Business model archetypes and pattern techniques. In our experiment we were able to use the Biogas 2020 BMES and project as a testbed. We report on our preliminary findings and draw some proposals to how the large scale experiment technical and BMI wise can be adjusted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Loebbeckea, C., & Picotb, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information System, 24(3), 149–157.

    Article  Google Scholar 

  2. Lindgren, P. (2017). The Business Model Ecosystem Journal of Multi Business Model Innovation and Technology, Vol. 4 2, 61–110. https://doi.org/10.13052/jmbmit2245-456x.421 River Publishers.

  3. Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194.

    Article  Google Scholar 

  4. Voss, N. J., & Tina, S. (2016). Fifteen years of research on business model innovation: How far have we come, and where should we go? Journal of Management. https://doi.org/10.1177/0149206316675927.

    Article  Google Scholar 

  5. Object Management Group. https://www.omg.org/about/.

  6. Lindgren, P., & Horn Rasmussen, O. (2013). The business model cube. Journal of Multi Business Innovation and Technology, 1(3), 135–182.

    Google Scholar 

  7. Lindgren, P. (2018). The multi business model innovation approach part 1 ISBN: 9788793609662. https://doi.org/10.13052/rp-9788793609655.

  8. Gassmann, O., Frankenberger, K., Sauer, R., Rechtschaffen, A., & Kales, A. (2016). Leading business model research the seven schools of thought. Chapter from exploring the field of business model innovation: New theoretical perspectives, Springer 2016.

  9. Matzler, K., Bailom, F., von den Eichen, S. F., & Kohler, T. (2013). Business model innovation: Coffee triumphs for Nespresso. Journal of Business Strategy, 34(2), 30–37. https://doi.org/10.1108/02756661311310431.

    Article  Google Scholar 

  10. Peter, L. (2016). Multi business model innovations towards 2050 and beyond, wireless world in 2050 and beyond: A window into the future!. ed./Ramjee Prasad; Sudhir Dixit. Springer, pp. 149–160 (Springer Series in Wireless Technology).

  11. Lindgren, P., & Ole, R. (2012). Business model innovation leadership journal of multi business model innovation and technology, pp. 53–69. River Publishers.

  12. Lindgren, P., & Jørgensen, R. (2012). Towards a multi business model innovation model. Journal of Multi Business Model Innovation and Technology, 1(1), 1–22.

    Google Scholar 

  13. Nunes, D., Zhang, P., & Sa Silva, J. (2015). A survey on human-in the loop applications towards an internet of all. IEEE Communications Surveys and Tutorials, 17, 2015.

    Article  Google Scholar 

  14. Krasimir, T., Lindgren, P., Manolova, A., Neshov N., & Poulkov, V. (2017). Digitizing human behavior in business model innovation. Preceedings GWS 2017, In Press.

  15. Video on the Biogas 2020 experiment. (2018). https://vimeo.com/244817125/e69cde4fd0.

  16. Insight personal profile system. (2018). https://www.insights.com/dk/.

  17. Taylor, L., Watkins, S. L., Marshall, H., Dascombe, B. J., & Foster, J. (2015). The impact of different environmental conditions on cognitive function: A focused review. Frontiers Physiology, 6, 372. Published online 2016 Jan 6. https://doi.org/10.3389/fphys.2015.00372.

  18. Feldman, L. A. (1995). Valence focus and arousal focus: Individual differences in the structure of affective experience. Journal of Personality and Social Psychology, 69(1), 153.

    Article  Google Scholar 

  19. Manolova, A., Neshov, N., Panev, S., & Tonchev, K. (2014). Facial expression classification using supervised descent method combined with PCA and SVM. In Proceedings of the biometric authentication in Lecture Notes in Computer Science, Springer, first international workshop BIOMET’2014, Sofia, pp. 23–24.

  20. Xuehan, X., & De la Torre, F. (2013). Supervised descent method and its applications to face alignment. In Proceedings of the IEEE conference on computer vision and pattern recognition.

  21. Penev, M., Manolova, A., & Boumbarov, O. (2014). Active shape models with 2d profiles for stress/anxiety recognition from face images. In Proceedings of the 9th int. conference on communications, electromagnetics and medical applications, Sofia, pp. 16–18.

  22. Sechkova, T., Tonchev, K., & Manolova, A. (2016). Action unit recognition in still images using graph-based feature selection. In IEEE 8th int. conf. on intelligent systems (IS), Sofia, pp. 4–6.

  23. Gassmann, O., Frankenberger, K., & Sauer, R. (2017). A primer on theoretically exploring the field of business model innovation.

  24. Valter, P. (2019). Novelty of laboratory measurement tools in multi business model innovation Aarhus Universitet.

  25. https://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/inso-2-2014.html.

Download references

Acknowledgements

This work was supported in part by the contract DN 07/22 15.12.2016 for research project: “Self-coordinating and Adaptive Wireless Cyber Physical Systems with Human in the Loop” of the Bulgarian Research Fund of the Ministry of Education and Science and in part by the EU Interreg fund with in the project Biogas 2020 project.

Author information

Authors and Affiliations

  1. Business Development and Technology, Aarhus University, Aarhus, Denmark

    Peter Lindgren & Per Valter

  2. Faculty of Telecommunications, Technical University of Sofia, Kliment Ohridski Blvd 8, 1000, Sofia, Bulgaria

    Krasimir Tonchev, Agata Manolova, Nikolay Neshov & Vladimir Poulkov

Authors
  1. Peter Lindgren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Per Valter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krasimir Tonchev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Agata Manolova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nikolay Neshov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vladimir Poulkov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Lindgren.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lindgren, P., Valter, P., Tonchev, K. et al. Digitizing Human Behavior with Wireless Sensors in Biogas 2020 Technological Business Model Innovation Challenges. Wireless Pers Commun 118, 2033–2053 (2021). https://doi.org/10.1007/s11277-019-06365-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06365-3

Keywords

Search

Navigation