The Internet of Things as Driver for Digital Business Model Innovation
4 IoT Business Model Innovation
IoT is a complex area from a technology perspective, creating new classes of applications such as the examples above. There are many business opportunities for technology companies, new application and service providers, and businesses that are developing new business models.
Business models seek to make sense of how businesses operate. In essence, they provide a hypothesis that needs to be implemented and
proven. They are presented at different levels of abstraction in the litera-ture. Magaretta (2002) discusses business models as narratives that describe the customer, customer value, revenue collection and delivery of value. Another level of abstraction is made by Gassmann et al. (2013), who describe the business model as an archetype of 55 different business model building blocks that can be combined in various ways to accom-modate how the business operates.
The most frequently adopted breakthrough on another level of abstrac-tion is the graphical framework. The most-cited of these is the Business Model Canvas by Osterwalder and Pigneur (2010), and this subsection focuses on analyzing frameworks at this level of abstraction.
4.1 IoT and Business Model Innovation
When it comes to innovation, IoT is a disruptive force, and traditional business model canvases are relatively weak in offering descriptive fea-tures to allow for IoT characteristics to be properly reflected (Mansour et al. 2018). The following characteristics are important for IoT-based business model innovation (BMI) and need to be able to be reflected.
BMI Transition
Many business models in the IoT cause a disruptive change in the core business of organizations. A business modeling process should be able to capture this transitional move from the as/is model to the to/be model.
As an example, the idea of predictive maintenance as mentioned above transforms the business model drastically. Whereas the as/is situation offers an opportunity for selling a product and then selling services to repair a product, the new business model encourages the use of pay-per- use or subscription-based models, including performance-based contracts to maintain products over a given period of time. This transition is not easily captured in BMI.
IoT Ecosystem, Technology Stack and Value Flow
IoT business modeling needs to be able to connect the IoT technology stack to the dimensions of the business model. Even in a simplified form the IoT technology stack should be able to be mapped, for instance to the ecosystem actors, technology providers and service providers. Other ele-ments that could be relevant are identifying the strengths and weaknesses regarding the coupling of technology choices in different layers, their interoperability and their switching costs. Taking the example of predic-tive maintenance, a service is typically enabled by a set of sensors measur-ing the state of a device, a communication network collectmeasur-ing data, a cloud platform storing and analyzing data, and the service itself. Businesses can of course make the investment to build an end-to-end system, but the trend is for more and more businesses to tend towards ecosystems when they are developing complex technology stacks, in other words by mixing and matching the different layers and components that are best suited for the application/service.
In this respect it is important to think of a network-based business modeling approach rather than a value chain or business-centric approach.
This points towards the idea of value flows and value networks, which describe how value can be shared by involved ecosystem members. Value flows might include revenue streams and costs as well as tangible and intangible assets.
Think Big Act Small/Lean Elements
It is also important for IoT business modeling to be able to think big but act small. The business model tool needs to be able to accommodate this principle in that it allows for drafting big visions, but breaks them down into smaller business models that facilitate development towards the greater vision. This might include learning in terms of technical compe-tencies and lean start-up ideas, such as fail forward and limited blast radius. It looks at elements of tactical and strategic business decisions.
4.2 IoT Business Model Innovation Canvases
Mansour et al. (2018) analyzes different business model canvases with respect to the previously mentioned characteristics.
• The business model canvas of Osterwalder and Pigneur (2010) is an industry standard, and offers an easy approach to BMI. The business model canvas has elements that allow for the mapping of technologies in terms of key resources and also of the ecosystem in terms of key partners, but lacks a mapping of the technology stack and network- based elements that is needed in developing IoT applications.
• The DNA model by Sun et al. (2012) builds on Osterwalder and Pigneur (2010), and brings forward a conceptual ecosystem perspec-tive by putting together the key partnerships, resources and activity blocks in one design block.
• The original purpose of the St. Gallen Business Model Navigator tool (Gassmann et al. 2013) is to allow businesses that are stuck in conven-tional thinking to think outside the box and create new revenue streams; it brings business modeling back to its basics, and with that it also remains technology agnostic.
• The Value Design Model of Westerlund et al. (2014) was created and discussed mainly on a conceptual level, which means it is lacking in areas such as usability and maturity. However, Value Design takes a step in the right direction, and will stimulate future research and devel-opment in IoT business modeling. It addresses and solves questions that were discussed by Sun et al. by illustrating the cost, revenue streams and other values in the IoT ecosystem, using Value Extract so that companies can profit from these values.
• The BM Type for IoT Model (Turber et al. 2014) brings forth an inter-esting aspect of how an IoT prototype artifact business model type visual layout can look. This model is in an early to mid-stage of devel-opment, which is why it lacks maturity and usability. The authors state that businesses need a better visual tool when it comes to IoT business modeling and thus propose a 3D model, assuming this helps.
• The 3DCM model (Chan 2015) includes aspects of strategy and tac-tics and increases usability by introducing a 2D-format. The 3DCM is an example of the foundational layer of IoT business modeling litera-ture. However, the new add-ons are inconsistent in addressing the issues stated by previous authors. For instance, revenue streams and cost structures between collaborators remains unclear. Moreover, no tools are provided, meaning the model lacks usability and maturity.
Osterwalder and Pigneur (2010) has until now been one of the most popular contributions to business modeling. Businesses use the business model canvas for technology-related business models as well. But to gain a full visual representation that is able to capture new values and revenue streams, the preference is for the model to acknowledge IoT as one way of doing business.
The first signs of a shift to IoT business modeling came from Sun et al.
(2012) who started to redefine the business model canvas into a new structure that implies the ecosystem perspective. More research was built upon the ideas about considering IoT as a way of doing business.
Gassmann et al. (2013) defined the four core building blocks for any business model, thereby creating space for new business model tools to arise. Westerlund et al. (2014), Turber et al. (2014) and Chan (2015) are all contributions to how an IoT business model tool can look.
4.3 Challenges in IoT Business Model Innovation
There are still no good tools available to capture the complexity of IoT business models. Mature but technology agnostic tools such as Osterwalder and Pigneur (2010) are used widely, and have brought about limitations to how IoT business models are developed (Vermesan et al.
2016). This is a mimicry of the technology challenges previously dis-cussed, specifically on the topics of interoperability and standardization.
It is only through standards for and the development of interoperabil-ity that we will be able to build more network-based business models. If we cannot imagine and communicate network-based business model because of a lack of tools, there will also be a lack of interest in focusing on standards and interoperability, continuing with common practices for
building silo applications, proprietary technology stacks and creating technology lock-ins. This also encourages the creation of data lakes and the aggregation of vast amounts of data, with the consequent ethical issues that are discussed in the following section.