A business model describes, in a model-like and holistic manner, the logical connections and the way in which a company generates value for its customers. A company can operate several business models at the same time. (see also https://en.wikipedia.org/wiki/Business_model)

One proven tool for analysing and shaping business models is the “Business Model Canvas”.

Added value can be understood as a process of increasing the perceived value of the product in the eyes of the consumers/customers. It is known as the value proposition. (Modified from https://en.wikipedia.org/wiki/Added_value)

The tangible dimension of value refers to physical products.  The intangible dimension of value refer to qualities that can be valuable to the (end) customer, they can be: durability, ethicality, aesthetic appearance, usability or some other personal need or value. Services, by definition, are intangible (non-material)

A revenue model describes the structure of how a company generates revenue or income. Each customer segment can contain one or more revenue streams.

A service model is the way that a firm offers intangible value to its customers. Different XaaS concepts describe broad category of service models, which offer customers product delivery and payment options that allow them to purchase access to products as a service. the most common ones are the three general cloud computing models: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS).  See also https://en.wikipedia.org/wiki/Cloud_computing#Service_models.

In the same way that software can be developed and commercialized using different business models according to the software ownership, digital platforms could be developed and commercialized using different business models according to the infrastructure ownership. Different infrastructure ownerships can be identified in this chapter and also their business models (like renting, pay per use…)

(From https://en.wikipedia.org/wiki/Business_ecosystem - definition by Moore)  A business ecosystem is an economic community supported by a foundation of interacting organisations and individuals - the organisms of the business world. The economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders. Over time, they coevolve their capabilities and roles, and tend to align themselves with the directions set by one or more central companies. Those companies holding leadership roles may change over time, but the function of ecosystem leader is valued by the community because it enables members to move toward shared visions to align their investments, and to find mutually supportive roles.

At the core of  all potential industrial use case scenarios of platforms are data. When formerly isolated data are shared, suddenly a new set of factors arises, both in terms of new external factors, but also in terms of business/microeconomic implications. Therefore, at the core of every digital platform must be a legally, organizationally and commercially viable concept for data sharing/trading/exchange.
When shaping this model, the following questions must be answered:

• What is the legal arrangement for data “ownership”?  Can users classify their data, is staggered approach possible (closed, traded or open data)? What are legal means that the platform uses to ensure the confidentiality of data ? (Trade Secrets, data base directive)
• Transparency: Can users monitor/control the sharing of data with third parties? Are there “expiration dates” for data use?
• Is the legal setting a fixed standards (“general conditions”) or is it a flexible, individual  approach? Are model contracts available?
• Are there sectorial regulatory requirements concerning data?
• How far is portability and change of platform possible?
• Who is responsible in the case of  breaches of confidentiality?
• How is fairness/ a level playing field between the platform and smaller players ensured ?

In general data liabilities refer to potential damages in relation to data characteristics (reliability and veracity promised, periodicity and velocity) and the stage in the process of data exploitation. for example in relation to quality of data, data, security breaches, delivery, data analytics misuse or misrepresentation, loss of stored data, access or retrieval of data, etc. In addition, the failure to follow and fulfill contractual obligations outlined in a given contract might lead to liabilities (see next section on industrial contract types for an outline).

Example of Liability Clause: Except in respect of death or personal injury caused by the Supplier’s negligence, the Supplier shall not be liable to the Customer by reason of any representation (unless fraudulent), or any implied warranty, condition or other Term, for any loss of profit or any indirect, special or consequential loss or damage (whether caused by the negligence of the Supplier, its servants or agents or otherwise) in relation to the supply of the Goods (or any failure to supply them) or their resale by the Customer, or otherwise arising out of or in connection with the agreement.