Digitalization of business models : impacts and opportunities for the healthcare industry : a multiple case study approach

The purpose of this applied-research project is to understand how digitalized business models can be assessed and analyzed with regard to their success in the broad field of healthcare. Digitalization holds enormous opportunities, but also requires a particular degree of tact and sensitivity concerning business model design. In order to propose specific guidelines for business model design this paper highlights the major dynamics of the digital business ecosystem, like turbulence, disruptiveness and blurred industry boundaries, and describes Omar El Sawy’s VISOR model, with its components Value Proposition, Interface, Service Platform, Organizing Model and Revenue Model. Following this, the main part consists of two case studies, Nike+ and Kinematix, and narrates the stories of one major, incumbent player and of one new entrant, with the purpose to achieve extensive insights on different perspectives of digital business models. Both companies operate in the field of E-health, more specifically running wearables, Nike with a more generic, Kinematix a more sophisticated approach. The main implications drawn from the case studies cover inter-industry partnership choices, the potential of motivational force based on Social Media, the transformation of the patient-caregiver relationship, the barriers to overcome originating from primary healthcare players, as well as specific dynamic and adaptive capabilities. The paper concludes with suggestions to stimulate future research to find solutions and ways to transform healthcare in a more advanced, forward-looking sector.Esta tese tem como objectivo perceber a aplicação de modelos de negócios digitais no sector da saúde e entender o seu sucesso. As oportunidades deste modelo de negócio são várias contudo o seu sucesso na aplicação requer sensibilidade e conhecimento especifico. Este trabalho propõe directrizes para lidar com este tipo de abordagem e realça as principais características deste modelo de negócio como turbulência, perturbações devido a esta nova visão de negócio, fronteiras da Indústria mal definidas e explica o modelo VISOR de Omar El Sawy em todas as suas componentes: Proposição de Valor, Interface, Plataforma de Serviço Modelo de Organização e de Receita. O corpo desta tese analisa os casos de estudo Nike+ e Kinematix descrevendo a história de um primeiro modelo já incumbente e de outro que é recente, respectivamente. Este processo pretende compreender duas estratégias diferentes em modelos de negócio digitais no sector da saúde. Ambas as empresas operam no segmento de E-saúde, especificamente roupa de corrida. Nike adopta uma abordagem mais genérica enquanto Kinematix tem uma abordagem mais específica. A principal implicação do estudo dos casos são parcerias inter-indústria, o potencial das redes socias em factores motivacionais, a transformação da relação paciente-cuidador, as barreiras a ultrapassar provenientes dos fornecedores de cuidados de saúde primários, bem como as dinâmicas e capacidades adaptativas. No fim deste trabalho são enumeradas sugestões para pesquisa académica futura com vista a transformar os cuidados de saúde num sector mais moderno e avançado

Barriers to IoT Business Model Innovation

The vision of an Internet of Things (IoT), in which virtually all physical things become connected to the internet, promises enormous economic potential. The IoT might disrupt entire industries and it forces companies to rethink their current business activities. In light of these challenges, research on business model innovation (BMI) can offer promising insights. This research paper aims to contribute to the emerging BMI literature by identifying innovation barriers in an IoT context. 16 barriers are identified on the basis of ten expert interviews that were conducted with employees from five multinational companies. The contributions of our study might lay a fruitful ground for future research, e.g. in respect to prescriptive IoT BMI processes or quantitative investigations of IoT success

Work extraction from unknown quantum sources

Energy extraction is a central task in thermodynamics. In quantum physics, ergotropy measures the amount of work extractable under cyclic Hamiltonian control. As its full extraction requires perfect knowledge of the initial state, however, it does not characterize the work value of unknown or untrusted quantum sources. Fully characterizing such sources would require quantum tomography, which is prohibitively costly in experiments due to the exponential growth of required measurements and operational limitations. Here, we therefore derive a new notion of ergotropy applicable when nothing is known about the quantum states produced by the source, apart from what can be learned by performing only a single type of coarse-grained measurement. We find that in this case the extracted work is defined by the Boltzmann and observational entropy, in cases where the measurement outcomes are, or are not, used in the work extraction, respectively. This notion of ergotropy represents a realistic measure of extractable work, which can be used as the relevant figure of merit to characterize a quantum battery.Comment: 5+1(Appendix)+7(Supplemental) pages, 4 figures. Comments and questions welcome. v2: changed the interpretation of the coarse-grained extraction operations (former eq (6), now eq (7)). Appendix with examples added. Results unchanged. v3: technical details moved into a short appendix. Added explanatory figure for the extraction operations. Added extra proof in the Supplemental Materia

Sparse Power Factorization: Balancing peakiness and sample complexity

In many applications, one is faced with an inverse problem, where the known signal depends in a bilinear way on two unknown input vectors. Often at least one of the input vectors is assumed to be sparse, i.e., to have only few non-zero entries. Sparse Power Factorization (SPF), proposed by Lee, Wu, and Bresler, aims to tackle this problem. They have established recovery guarantees for a somewhat restrictive class of signals under the assumption that the measurements are random. We generalize these recovery guarantees to a significantly enlarged and more realistic signal class at the expense of a moderately increased number of measurements.Comment: 18 page