Revised value co-creation domains in a Machine Learning-enabled context
| Domain | Definition | Findings |
|---|---|---|
| VCC driver | Expected benefits the customer can gain from using the digitally-enabled PSS, and the needs that can be predicted via ML before they become explicit | Expected and predictable benefits that the manufacturer, the customer and the entire ecosystem can gain from using the digitally-enabled PSS |
| Resource used | Intangible resources, such as existing skills and knowledge embedded in goods, and brand-new knowledge that ML algorithms and related software are able to create within a virtuous cycle of resource generation and development | Knowledge embedded in goods that ML algorithms and related software are able to create and share between the manufacturer and client, within the manufacturer's ecosystem, and across the manufacturer's ecosystem and the clients' one |
| Role of goods | Bi-directional vehicles of resources exchanges, through which the manufacturers transfer their skills and knowledge to the customer and collect data to be turned into new knowledge | Multi-directional vehicles of resources exchanges, that involve a larger number of value co-creators and beneficiaries that includes also new player from outside |
| Role of value co-creators | All the actors that interact within the ecosystem by sharing data to be processed via ML become simultaneously value co-creators and beneficiaries of the co-created value | Each actor that interacts within the ecosystem differently contribute to VCC basing on their degree of power in knowledge management, which depends on the digital maturity and the accountability related to the ML logic |
| Role changes (new domain) | – | Digital servitization via ML imposes to each co-creator to acquire new skills and competencies, and to redefine the way in which they relate to each other |
| Purpose and measurement of co-created value | ML helps in translating the concepts of “adaptability, survivability and system well-being” in terms of more tangible dimensions that can be measured to express the degree to which such purposes have been achieved | ML helps the manufacturer in: (1) avoiding oversizing matters; (2) reducing the costs of repairing interventions; (3) constituting new SBUs, such as the digital innovation department; (4) promoting higher client satisfaction and loyalty ML helps the clients in: (1) reducing machines' downtime; (2) maximizing productive capacity levels; (3) overcoming labors' related biases; (4) benefiting from networked information and knowledge |
| Process of VCC | Set of concurrent activities to be analyzed dynamically according to at least two perspectives: that of the manufacturer and that of the customers | Set of concurrent activities to be analyzed dynamically according to at least two perspectives: that of the manufacturer and that of the customers |
| Contingent forces | Exogenous and endogenous forces that lead to the adoption of a specific configuration among the finite number of possible ones | Exogenous forces: (1) Availability of technological advancements Endogenous forces: (1) manufacturer's mission and vision; (2) manufacturer's leadership style; (3) digital maturity of clients and of ecosystem's actors |
| VCC driver | Expected benefits the customer can gain from using the digitally-enabled PSS, and the needs that can be predicted via ML before they become explicit | Expected and predictable benefits that the manufacturer, the customer and the entire ecosystem can gain from using the digitally-enabled PSS |
| Resource used | Intangible resources, such as existing skills and knowledge embedded in goods, and brand-new knowledge that ML algorithms and related software are able to create within a virtuous cycle of resource generation and development | Knowledge embedded in goods that ML algorithms and related software are able to create and share between the manufacturer and client, within the manufacturer's ecosystem, and across the manufacturer's ecosystem and the clients' one |
| Role of goods | Bi-directional vehicles of resources exchanges, through which the manufacturers transfer their skills and knowledge to the customer and collect data to be turned into new knowledge | Multi-directional vehicles of resources exchanges, that involve a larger number of value co-creators and beneficiaries that includes also new player from outside |
| Role of value co-creators | All the actors that interact within the ecosystem by sharing data to be processed via ML become simultaneously value co-creators and beneficiaries of the co-created value | Each actor that interacts within the ecosystem differently contribute to VCC basing on their degree of power in knowledge management, which depends on the digital maturity and the accountability related to the ML logic |
| Role changes (new domain) | – | Digital servitization via ML imposes to each co-creator to acquire new skills and competencies, and to redefine the way in which they relate to each other |
| Purpose and measurement of co-created value | ML helps in translating the concepts of “adaptability, survivability and system well-being” in terms of more tangible dimensions that can be measured to express the degree to which such purposes have been achieved | ML helps the manufacturer in: (1) avoiding oversizing matters; (2) reducing the costs of repairing interventions; (3) constituting new SBUs, such as the digital innovation department; (4) promoting higher client satisfaction and loyalty |
| Process of VCC | Set of concurrent activities to be analyzed dynamically according to at least two perspectives: that of the manufacturer and that of the customers | Set of concurrent activities to be analyzed dynamically according to at least two perspectives: that of the manufacturer and that of the customers |
| Contingent forces | Exogenous and endogenous forces that lead to the adoption of a specific configuration among the finite number of possible ones | Exogenous forces: (1) Availability of technological advancements |
Source(s): Author's own creation
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