Chapter 4 METHODOLOGY
4.3 Arising Level of Network Effect Factors’ Testing Module
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Figure 4.2.1 Digital Ecosystem Development with Network Effect
The following section will go into details and explicate the operation of these modules and sub-modules.
4.3 Arising Level of Network Effect Factors’ Testing Module
The core value of Arising Level of Network Effect Factors’ Testing Module is to ensure the digital ecosystem evolution is operating on the proper track toward the correct direction. To examine the influence from the D3 Accelerator Assistant Module to the designed ecosystem, to improve the D3 Accelerator Assistant Module, and to look over the network effect circulating within designed ecosystem at last, are the main functions of this module. These functions will only be started up if the designed ecosystem is capable of reaching the critical mass, that is to say it is qualified to estimate arising level of network effect factors.
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Network effect level is a measurement that designers can comprehend the network effect of the digital ecosystem goes along to what extent. When we ponder over the level of the network effect of the designed ecosystem, we are trying to do the improvement of the designed pattern based on three dimensions of D3 Accelerator Assistant Module. Moreover, we believe the improvement of those dimensions is related to the network effect level. Figure 4.3 shows illustrate the interaction between D3 Accelerator Assistant Module and the network effect, and the following three sections are going to explain the cause and effect of assistant module’s improvement and the level of network effect.
Figure 4.3 Relationship between Assistant Module and Network Effect
4.3.1 Completeness of Operant Configuration
In this sub-module of Destination Value Configuration of digital Ecosystem Entities, the output value represents the consensus level of digital ecosystem. In other hand, the consensus level of digital ecosystem is to examine the interactions between
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stakeholders and also, the designed ecosystem will have higher successful rate with the higher level of consensus. Consensus level is not only related to the successful rate of digital ecosystem, but also highly correlated to the operating level of network effect.
Due to the consensus of ecosystem cognition of stakeholders, the identification of the digital ecosystem will be consistent in every stakeholder’s mindset. They share the same vision and recognize the equivalent prospect, that is to be conductive to the interactions within the stakeholders of digital ecosystem from different perspective or different side. Consequently, we believe that to improve the consensus level of the Completeness of Operant Configuration could help increasing the cross-side network effect of the digital ecosystem.
In addition, we calculate the extent of network effect from the input of three dimensions, divided into two parts of variables that can affect same-side network effect and cross-side network effect (the function is shown in Formula 4.4.1). The sum of the variables represents the fulfillment of the network effect of digital ecosystem, that can be a feedback for designers to better understand whether their design corresponds to the service demand and that is to perform an immense network effect.
Formula 4.4.1 Arising Level of Network Effect Factors
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4.3.2 Degree of Operant Empowerment
The output value of the empowerment degree on behalf of the smart properties and resources integration and empowerment can be regarded as the willingness to license the smart properties and resources for other stakeholders. When we are going to enhance the network effect, first we have to make sure there is a powerful motivation attracting people to join the digital ecosystem. That means every participant can draw benefits which is not limited to the access of money from the ecosystem. We believe that the improvement of smart properties and resources integrating can make the motivation of participant more powerful and that will also result in the positive impact on both same-side and cross-side effect.
After creating enough motivations, we devote to transform the passive reception into active sharing, rely on the authorization of stakeholders’ own smart properties and resources to facilitate the digital ecosystem operation based on the network effect. The value of the degree of operant empowerment contains the willingness to devote one’s owns to others, that is to say, the improvement of the degree of operant empowerment can support the same-side effect and the cross-side effect for increasing the smart properties and resources that can be utilized and also stakeholders’ willingness to share.
The function shown in Formula 4.4.1 as the above, also provides a useful feedback for designers to connect the empowerment degrees to the network effect of digital ecosystem, in light of the Fulfillment of Critical Mass that has be mentioned in the first section of chapter 4.2, especially the negative effect and the probability of participants together with the resources integration and willingness to share. They can give designers some inspiration of how to regulate the variables that can increase the fulfillment of critical mass and can enhance the network effect. That is a serviceable information which can be used in further advanced digital ecosystem design by
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monitoring the variation of variables from the Degree of Operant Empowerment and the network effect of whole digital ecosystem.
4.3.3 Operation Flow of Linkware Design
The output of the Operation Flow of Linkware Design is a concept of the fulfillment of the service flow level. Operation Flow of Linkware Design considers about the elements of fulfilling flow experience, participants perceived service quality, cost of designing the linkware, and the interaction complexity, and so on. All the above is to lower the barriers for stakeholders to entry the digital ecosystem, so that we can attract more participants to join the digital ecosystem. In this way, that is somehow to bring the positive impact on the same-side and the cross-side network effect for the digital ecosystem.
The same as the function shows in Formula 4.4.1, the arising level of network effect factors measures the entirety of service flow completeness, in light of melioration of the advanced ecosystem design, completeness of service flow and the consideration of barriers to entry, illustrated in Figure 4.3, allude the tendency of service design’s probability. We believe the outcome of the Network Effect Testing Module can help designers to do the amendment while proposing the improvement program in advance.
Furthermore, the feedback also can assist designers to master the dynamic equilibrium relationship between linkware design and the critical mass.