Advanced Business Model Innovation Supported by Artificial Intelligence and Deep Learning
- 888 Downloads
Businesses have classically put emphasis on human bonds related to their BM’s [www.conansence.org]. By the fast development of more sensoring, persuasive and virtual BMs increasingly run autonomously by machines, businesses should expect to be able to, build competence and thereby be capable in the future to innovated BM’s and operate BM’s in new types Business Model Ecosystems (BMES) (Lindgren and Rasmussen in J Multi BMI 4:1, 2016) in the future—where physical, digital and virtual BMES become integrated. This will investable open up to new multi business model potential but also require that businesses operate and innovate their multitudes of BM’s differently. BMES and BM’s (Lindgren in J Multi Bus Model Innov Technol 4:1, 2016; Lindgren and Rasmussen in J Multi Bus Model Innov Technol 1: 135, 2013) have for a longtime been based and built up with mainly human bond communication, but new technologies very much based on machine to human communication and machine to machine communication evolves and change the game of BMI with exponential speed. How will this change the game of Business Model Innovation (BMI) between humans, humans and machines and machines to machines. How will this evolvement influence businesses ability to “download”, “see”, “sense”, “relate” and “receive” and relate BM’s with their AS IS and TO BE BM’s. The paper addresses the exponential development of artificial intelligence technologies, persuasive technologies, virtual technologies and thereby increase the potential to create, capture, deliver, receive and consume physical, digital, persuasive and virtual BMs in Business model innovation and introduce a conceptual model to future business model innovation and operation.
KeywordsAdvanced business modelling AI Deep learning Business model innovation Sensors Persuasive technologies Physical Digital Persuasive and virtual business models
- 1.Lindgren, P., & Rasmussen, O. H. (2016). The business model ecosystem. Journal of Multi BMI, 4, 1.Google Scholar
- 3.Lindgren, P., & Rasmussen, O. H. (2013). The business model cube. Journal of Multi Business Model Innovation and Technology, 1, 135.Google Scholar
- 4.Lindgren, P. (2017). Advanced business model innovation. Journal of Wireless Communication, 4, 71.Google Scholar
- 5.Prasad, R. (2016). Knowledge home. In International conference on advanced computer science and information systems (ICACSIS) (pp. 33–38). Malang: IEEE Explorer.Google Scholar
- 8.Lee, H. L. et al. (2016). Technological disruption and innovation in last-mile delivery. Stanford Business (pp. 1–26). Available at: https://www.gsb.stanford.edu/sites/gsb/files/publication-pdf/vcii-publicationtechnological-disruption-innovation-last-mile-delivery.pdf.
- 9.Valter, P., Lindgren, P., & Prasad, R. (2017). Artificial intelligence and deep learning in a world of humans and persuasive business models. In GWS 2017 Proceedings IEEE Explorer (in press).Google Scholar
- 11.Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., Kantarci, B., & Andreescu, S. (2015). Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: Opportunities and challenges. In 2015 IEEE 12th international conference on services computing (Scc 2015, pp 285–292).Google Scholar
- 13.Ohshima, T., Satoh, K., Yamamoto, H., & Tamura, H. (1998). AR2 Hockey: A case study of collaborative augmented reality. In Proceedings of IEEE 1998 virtual reality annual international symposium (Cat. No. 98CB36180) (pp. 268–275).Google Scholar
- 14.Shatte, A., Holdsworth, J., & Lee, I. (2014). Hand-held mobile augmented reality for collaborative problem solving: A case study with sorting. In 2014 47th Hawaii international conference on system sciences (Hicss) (pp. 91–99).Google Scholar
- 15.Tu, M. R., Chang, Y. K. & Chen, Y. T. (2016). A context-aware recommender system framework for IoT based interactive digital signage in urban space. In Second international conference on IoT in urban space (Urb-Iot 2016) (pp. 39–42).Google Scholar
- 19.Valter, P., Lindgren, P., & Prasad, R. (2018). The consequences of artificial intelligence and deep learning in a world of persuasive business models. IEEE Aerospace and Electronic Systems Magazine (in press).Google Scholar