Mechanisms for Business Ecosystem Members to Capture Value through the Strong Network Effect

Business ecosystem leaders tend to focus on their own success rather than carefully monitoring the success of business ecosystem members, and each member must find a mechanism to capture value. Rarely has business ecosystem research examined the success of business ecosystem members. This study investigates the mechanisms by which business ecosystem members capture value in the business ecosystem related to different types of storage formats and how these mechanisms contribute to the sustainability of the business ecosystem. We identify two value capture mechanisms in the storage business ecosystem. First, media manufacturers, being members of the storage business ecosystem, can capture value through strong network effect in the server backup markets. They can sell a significant amount of media for every single unit of a drive sold through the strong network effect. Second, media manufacturers can capture value by selling their media repeatedly as consumables for many years. We quantitatively confirm these mechanisms using a vector autoregressive model to measure the strength of the indirect network effect. These mechanisms help business ecosystem members in staying within the business ecosystem and also contribute to the business ecosystem’s sustainability

The Mechanisms for Business Ecosystem Members to Capture Part of a Business Ecosystem’s Joint Created Value

Research into business ecosystems has rarely examined the success of business ecosystem members. Business ecosystem leaders tend to focus on their own success rather than carefully monitoring the success of business ecosystem members, and each member must find a mechanism to capture part of the business ecosystem’s joint created value. This study examines the mechanisms by which business ecosystem members capture part of a business ecosystem’s joint created value in the cases of linear tape open (LTO) ecosystems and how these mechanisms contribute to the sustainability of a business ecosystem. A case study was conducted with a review of both the author’s experience with Sony and third-party resources. We confirm the results by panel data analysis. We identified three mechanisms. First, a business ecosystem member can establish a new business ecosystem on their own through newly created complementary innovation. Essentially, a business ecosystem member can become a business ecosystem leader in a new business ecosystem. Second, a business ecosystem member gains market shares from technology leadership, the experience of mass production, and collaboration with the business ecosystem leader. Third, a business ecosystem member who creates complementary innovations can obtain patent royalties. These mechanisms help business ecosystem members stay within business ecosystems and contribute to its success and sustainability

Detection of Spinal Muscular Atrophy Patients Using Dried Saliva Spots

Spinal muscular atrophy (SMA) is a lower motor neuron disease, once considered incurable. The main symptoms are muscle weakness and muscular atrophy. More than 90% of cases of SMA are caused by homozygous deletion of survival motor neuron 1 (SMN1). Emerging treatments, such as splicing modulation of SMN2 and SMN gene replacement therapy, have improved the prognoses and motor functions of patients. However, confirmed diagnosis by SMN1 testing is often delayed, suggesting the presence of diagnosis-delayed or undiagnosed cases. To enable patients to access the right treatments, a screening system for SMA is essential. Even so, the current newborn screening system using dried blood spots is still invasive and cumbersome. Here, we developed a completely non-invasive screening system using dried saliva spots (DSS) as an alternative DNA source to detect SMN1 deletion. In this study, 60 DSS (40 SMA patients and 20 controls) were tested. The combination of modified competitive oligonucleotide priming-polymerase chain reaction and melting peak analysis clearly distinguished DSS samples with and without SMN1. In conclusion, these results suggest that our system with DSS is applicable to SMA patient detection in the real world