All innovation begins with vision. It’s what happens next that is critical.
Entrepreneur and author
in The Lean Startup
This article develops and showcases the viability radar, which is designed to assess the innovation potential of transformative service ideas. Based on service research and innovation literature, we highlight the importance of novel simplifying technology, supporting value networks, cost-effective business models, and regulatory environments that enable the renewal of prevailing market practices. We operationalize the radar with a set of questions and assess the innovation potential of three pilot cases of new transformative healthcare services.
In healthcare, the need for innovative services is acute (Busse et al. 2010; Currie & Seddon, 2014), especially due to the aging population (Christensen et al., 2009; WHO, 2015a) and increasing incidence of lifestyle diseases (WHO, 2015b). Service science and innovation scholars have identified improving well-being through transformative service as one of the top research priorities (Ostrom et al., 2010). Transformative services aim at changes in society and the economy, not only changes in science and technology (Sen, 2013). By transformative, we mean innovations that make a marked change in the well-being of the service ecosystem. Such change may be radical (disruptive) or it may comprise a series of incremental changes. We acknowledge that there is an abundance of seemingly good ideas that suggest how technology or process reconfigurations could be employed to increase well-being in the health care context. Nevertheless, before these ideas can be referred to as innovations, they need to be accepted and adopted in parallel by multiple stakeholders in the ecosystem, such as the service provider’s management and employees, service purchasers, authorities, and consumers (Heikkilä & Kuivaniemi, 2012). And, they then need to be diffused through market practices by institutionally embedded actors. Many actors could benefit from an approach that would help predict an idea's value and potential. This article attempts to provide one such approach.
The objective of this study is to increase understanding of institutionalization in transformative service innovation processes in the context of healthcare. The major contribution of our article is that it operationalizes the model of four innovation elements, inspired by the work of Clayton Christensen and colleagues (2007, 2009) to analyze the different extents of viability in real-life service transformations. The study introduces a simple template for viability evaluation – the viability radar – consisting of metrics on: i) the novelty and simplicity of the technology, ii) the feasibility of the business models to the partners, iii) the supporting value networks, and iv) the regulatory environment, enabling renewal of the prevailing market practices. By viability, we refer to a service innovation that includes a novel idea that can be deployed into practices that increase well-being in the service ecosystem; is accepted and adopted by different stakeholders; and has suitable features to attract diffusion in its innovation and stakeholder networks. The usage of the radar is showcased through three healthcare cases assessing the viability of the innovations.
This article is organized as follows. First, we describe the relevant innovation literature and institutionalization processes to understand how innovations spread and advance consumer and societal well-being. Thereafter, we operationalize a viability radar template that can be employed to assess the viability of potential disruptive service innovations. In the empirical part, we showcase the developed viability radar by assessing three potential healthcare innovations that aim at improving well-being through increased efficiency and empowerment of patients. In addition to showing how the template was used in evaluating the viability of these cases, we discuss how the template could be developed further.
The literature characterizes innovation as a multi-stage activity whereby organizations transform ideas into new or improved products, services, or processes and bring them to market (Thompson, 1965; Hauser et al. 2006). It is also a way for organizations to advance, compete, and differentiate themselves successfully in their marketplace (Baregheh et al., 2009). The key matter is that innovation is expected to substitute existing solutions. Some researchers (e.g., King & Anderson, 2002; Kraus et al., 2011) state that innovation should pose novelty and tangible, recognizable qualities as something other than just a change to the typical routines.
There is rich literature following Rogers’ (1995) innovation diffusion model comprehending the adoption processes across several individuals over time (Robert et al., 2010). For example, Caldwell and Kleppe (2010) underscore that public demonstration by early adopters reduces consumer resistance to HIV/AIDS public health innovations. Also the readiness of both health service providers (Okazaki & Castañeda, 2013) and early-adopting patients (Lanseng & Andreassen, 2007) in adopting new technologies has been studied. Similarly, Okazaki and colleagues (2013) focus on perceptions of the technology as well as personal characteristics of the physicians.
Information and communication technology (ICT) is considered a main driver of innovation, because its transformational effects spread to several sectors of the ecosystem and society (Dutta & Bilbao-Osorio, 2012). Also, in healthcare, ICT solutions are expanding rapidly (Currie & Seddon, 2014, Dobrev et al., 2010; Ho, 2007). Healthcare is shifting towards personalized services (Seppälä et al., 2012) and eHealthcare with a wide range of ICT solutions from remote medical monitoring to emergency alarm services (Oh et al., 2005).
However, innovation scholars point out that ICT is not sufficient alone, but should be accompanied by innovative business models that release the value potential of the new technical invention by commercializing it to markets (Chesbrough & Rosenbloom, 2002; Rayna & Striukova, 2014; Shin, 2014). The business model describes the general logic of business, including customer segment(s), service, organization, technology, and financing (Bouwman et al., 2008). That is, a business model can be seen as a representation of the strategy and as the starting point for planning operative business processes in selected markets (eFactors, 2002). The markets are especially complex in closely regulated economic sectors, such as in healthcare, where we are expecting innovations to simultaneously create economic and societal value (Rohrbeck et al., 2013).
Many practitioners point out that it is rather easy to come up with new ideas, but the real challenge is putting them into practice. Designing a business model and institutionalizing it is especially demanding when innovations occur outside the exclusive control of traditional firm boundaries (de Reuver et al., 2013; Muegge, 2011). Research shows that diffusion of innovations in healthcare in particular requires a credible evidence base (Barnett et al., 2011), observability, strong leadership and trust (Berwick, 2003), and it also requires strong social interactions between professional groups and suitable organizational contexts (Barnett et al., 2011; Fitzgerald et al., 2002). Often, the needed changes are of a systemic nature (Dubosson-Torbay et al., 2002) and require a business ecosystem (Moore, 1993) where multiple organizations act in collaboration (Rohrbeck et al., 2013), mixing the traditional boundaries of business sectors and of companies, and involving users in co-creation (Heikkilä & Kuivaniemi, 2012; Lettl et al., 2006; McColl-Kennedy et al., 2012). For instance, Heikkilä and colleagues (2014) evaluate the feasibility of a networked business model designed jointly by several partners for an innovative health service concerning physical activity prescriptions, and Nikayin, Heikkilä, de Reuver, and Solaimani (2014) discuss its social implications. Although these studies increase our understanding of the behaviour of pioneers in adapting healthcare innovations, they are limited in terms of raising awareness of the institutionalization of healthcare innovations into market practices.
Christensen and colleagues (2009), in their book on disruptive innovations in the healthcare sector, summarize the innovation literature discussion into four elements of innovation: i) sophisticated and simplifying technology, ii) innovative business models, iii) an economically coherent value network, and iv) regulations and standards. According to Christensen, traditionally, new solutions are typically first adopted only by the top level of users. In the healthcare sector this means, for example, that university hospitals are the first adopters of new technologies, often with heavy costs. Thereafter, the innovation is slowly diffused to other healthcare actors. His theory of disruptive innovations emphasizes that some technologies are able to simplify and routinize such processes, which have previously been more complex or intuitive. Moreover, disruptive innovations also require business model innovations that deliver value to customers profitably and an ecosystem with a commercial infrastructure that supports diffusion. Prevailing regulations and standards within the ecosystem can either ease or restrict the needed reconfigurations.
The work of Christensen and colleagues (2009) is often referred to and most of these articles describe a specific innovation, arguing that it has the potential to become disruptive (e.g., Hahn et. al, 2014; Rapoport et al., 2011; Wessel & Christensen, 2012). For instance, several articles identify healthcare clinics within retail establishments as a disruptive innovation (e.g., Burns et al., 2011; Grady, 2014, Kissinger, 2008). Also genomics, personalized medicine, and pharmacogenomics are identified as being disruptive (e.g., Carlson, 2009; Schulman et al., 2009; Wade et al., 2014). Other articles are typically general commentaries or conceptual papers attempting to extend, modify, or supplement the Christensen analytical framework (Rapoport et al., 2011). Criticism has grown in the literature, especially towards its strong ex-post perspective (Danneels, 2004; Govindarajan & Kopalle, 2006; Keller & Hüsig, 2009; Klenner et al., 2013). For example, Tellis (2006) questions the predictive value of the concept if one must wait until the disruption has occurred. Even though there are already a number of approaches proposed for ex-ante analysis, such as differing classification analyses, economic models, and scenario methods (Klenner et al., 2013), they are mostly focusing on macro-level analysis of transformative or disruptive innovations. We believe that Christensen and colleagues (2009) provide a suitable framework for micro-level ex-ante analysis of innovation pilots and proposals. We therefore developed an ex-ante viability template for organizations and funding institutes to evaluate the innovations, and to spot specific dimensions requiring further development if they wish to further advance the diffusion and institutionalization of the innovation.
Research Approach and Methodology
This study follows a design science approach, which has its roots in the pragmatist research philosophy (Hevner, 2007; Iivari, 2007). This approach is used especially by information systems researchers studying creation, transfer, and diffusion of innovation in organizations and society (Anderson et al., 2012; Leung et al., 2013; Venable et al., 2010). It is considered as a new means for improving the relevance of research as it focuses on building artefacts (in this article the viability template), using the artefacts to solve relevant problems, and learning from the use of the artifacts (Venable et al., 2010). Design science is solution-oriented, linking interventions to outcomes (Van Aken & Romme, 2009), and solutions follow the logical statement “If you want to achieve Y in situation Z, then you perform something like X”. X can be an act or a sequence of acts, but it can also be the design and implementation of some process or system. In this article, we formulated the statement as follows: If managers want to select and advance the most viable innovations, then the viability radar will help them to identify and measure the viability of innovations and to analyze which innovation element(s) affect(s) viability.
Even though almost any type of research method can be applied in design science research, studies are typically case-based, collaborative, and interventionist (Van Aken & Romme, 2009). Our study is an interventionist multiple case study where researchers were collaborating with the organization in developing actual solutions to problems and contributing both to theory and practice (Dumay, 2010; Lukka & Suomala, 2014). Typically, design cases are different from ordinary case-study research, which is focused on generating in-depth knowledge of a certain phenomenon with a given context. Design cases aim at knowledge on how unique artefacts are created in the context and how the artefact and design process can be reused and theorized.
Our empirical study was commissioned by Sitra, a national innovation fund institute that promotes projects aiming for sustainable well-being in Finland. One of its divisions aims to contribute to the development of user-friendly electronic services for health promotion and to create conditions for Finland to become a pioneer in electronic welfare. The division has executed its mission by sponsoring research in the theme, influencing opinions, and launching and funding experimental projects where new innovative ideas are put into practice and evaluated. After running several pilot projects in health and well-being, the institute recruited the researchers to help analyze the viability of their ongoing and future pilots. The aim was to generate practices for the funding institute to estimate the potential viability of innovation pilots (i.e., its strengths and weaknesses) and to focus their efforts on advancing the diffusion of healthcare innovations. In collaboration with Sitra, three pilot services were selected as interesting examples of potential transformative health care reforms:
- An electronic maternity card
- An electronic tool for assessing the need for medical care for birth control, eating disorders, and cracked teeth
- An electronic service to motivate senior citizens to do physical exercises
Research Process and Data Collection
Table 1 shows the process, tasks, and data produced or collected in the project. The process consists of six steps adapted from Verschuren and Hartogh (2005).
Table 1. The research process
Research Data Sources
Requirements and assumptions
Identifying the solution
Prototype of the template
Our assessment of the cases is based on 12 interviews of the service providers, system providers, and responsible project leaders at the funding institute (Table 2). Prior to the interviews, a case study protocol and an interview protocol were developed to guarantee research reliability (Yin, 2004). During the interviews, we followed a semi-structured format to discuss differing aspects of the innovation. Each interview took from one hour to two and a half hours, and all interviews were recorded. During the interviews, several memos were made regarding meta-information, including the emphasis, reactions, and expressions of the interviewees, and the key concepts being discussed. After the interviews, essential topics that were discussed during the interview were collected in table format. In order to triangulate (Yin, 2004), multiple data sources were used, including company websites, documents regarding stakeholder analysis, business and market analysis reports, and other relevant documentation such as material provided by the institute (e.g., contracts, minutes of a board meeting, and final reports when available) to justify our assessment.
Our interview data is solely based on the viewpoints of service and system providers. However, to overcome the absence of the end-customer view, we had access to consumer satisfaction survey results conducted in two of the cases, and we tested all services ourselves as well. With this data, we could estimate the acceptance of the service by the end users.
Table 2. Details of interviewees
Interviewees By Position
Electronic maternity card
Head Nurse, Maternity Clinic
Executive Medical Director, Maternity Clinic
Coordinator, IT Management of City T
Chief Executive Officer, IT Company A
Project Manager, IT Company A
Medical care need assessment
Director of Development, Medical Care Organisation
Project Manager, IT Company B
Senior tablet computer service
Director of Development, City J
Project Manager, University
The Result: Viability Radar
As a foremost outcome of the interviews, we recognized some key similarities in the factors affecting the viability of the pilots. Given that these factors could be linked with relevant themes in the innovation literature, we decided to present the key elements with a graphical template – the viability radar – covering the essential elements for viability of a healthcare innovation. It should be noted that we did not have the viability radar construct ready when we started the empirical study, but it was created during the process. Building on the previous literature and discussions with the funding institute, it became clear that the degree of technological innovation has to be estimated in combination with the business models. Moreover, because the service providers and services in healthcare are largely interconnected through joint processes and ICT, the business models have to be feasible to all partners. Furthermore, the diffusion of healthcare innovations is strongly regulated by laws and practices of the trade.
We operationalized the template by assigning a few fundamental questions to each of the four elements. As a practical tool, the simple viability radar is designed for assessing the innovation potential of transformative service ideas in at least three scenarios. First, it may be used for funding decisions to cherry-pick which innovation proposals have the most diffusion potential. Second, it may be used to focus attention on the elements of viability that are lagging the furthest behind. Third, it can be utilized in business development by increasing the overall understanding of the potential barriers for diffusion in the wider institutional setting.
Next we describe and justify the set of questions for each element.
To assess whether solutions enable some processes to be carried out in a simpler or more effective way, we focus on value-in-use-in context (Vargo & Lusch, 2008). The new technology enables value creation either by reaching a new performance level in some respect or by simplifying previously used methods. When renewing healthcare services, the substitution is a very important feature. If the innovation does not replace any older functions, its adoption would only increase the service system’s size unless it enables a very novel and radical value increase (Baker et al., 2003). Overlapping information systems and double bookkeeping of health information entries is a typical example of uncompleted substitution (Miller & Sim, 2004). Therefore, the elemental questions are:
- Is the innovation a substitute for existing services or functions?
- Is the innovation significantly more novel and better performing than previously used practices?
Business model (BM)
The service provider needs to have a functional business model that will provide added value to the end customers. We also extend the business model to cover the incentives of different stakeholders to change their behaviour in accordance with the innovation. Thus, we expand the view to value co-creation opportunities with users (Tanev et. al, 2014) and within the network of partners (i.e., collaborative business model innovation: Heikkilä & Heikkilä, 2013). Willingness of these key stakeholders to adopt the use of reform is crucial for its viability. Decision making often becomes monetized, requiring calculations and a proof of concept to show that that the innovation's adoption will lead to a positive surplus compared to the existing situation (Heikkilä et al., 2005).
- Does the current service provider see the opportunity for benefits to overcome the costs?
- Do the suppliers see opportunities to generate business growth?
- Are the consumers and end users adopting and committing to use the innovation?
Value network (VN)
If the innovation does not diffuse to other organizations, it can easily be seen only as an experiment and it will not reach its full coverage. Here, it is emphasized that support for innovation diffusion can only be expected when multiple stakeholders experience mutually beneficial outcomes (Maglio & Spohrer, 2013). Viable innovation requires that there are no major conflicts of interest among different stakeholders around the innovation. Mutual understanding of the goals and motives of each partner helps innovation adoption and diffusion considerably. Low need for modifications and customization implies a greater simplicity of innovation and therefore greater chances for diffusion (Rayna & Striukova, 2014).
- Are there supportive partners and interest groups for the innovation and its implementation?
- Do the goals and objectives of the participating organizations support each other?
- Can the innovation also be utilized in other contexts (with only slight customizations)?
Regulation and standards (R)
Rules determine what kinds of changes are allowed and what are not. Thus, the viability radar takes into account not only various stakeholders but also the influence of institutions, enabling and constraining value co-creation, and the diffusion of market practices (Akaka et al., 2013). Rules, standards, and legislation are society’s formal means to ensure fair, safe, and ethical courses of action. Naturally, they are drawn up only after the emergence of an innovation. Informal routines and practices are rooted in the organisation's culture, and changing them requires recurrent communication and demonstrations.
- Does the realization of the innovation have any legal or regulative obstacles?
- Does the innovation fit into existing practices or are the practices changeable?
The questions may be developed further but, as such, they synthesize the important themes raised in transformative service research and in the innovation literature, as well as from institutional theories. To keep it simple, the measurements can be subjective red-yellow-green status estimates or more elaborate quantitative values. If there is a need to perform an in-depth analysis of the viability of the innovation, the template helps to focus additional studies or pilots, etc. to provide more information for the basis of the evaluation. In Figure 1, we present the viability radar using data from a hypothetical innovation. The further away from the centre the values reside, the better the chances that the innovation has to become widely adopted and eventually institutionalized into the practices in the healthcare sector.
Figure 1. Viability radar of transformative service innovations with a hypothetical example of an innovation. The outer circle represents the most successful premises for viability, and the innermost circle refers to the situation, which demands considerable attention for problem solving.
Evaluation of the Cases with the Viability Radar
We applied the viability radar to assess the case innovations, which were selected together with Sitra. The results presented in Figure 2 describe our interpretation of the status of the pilot regarding viability questions. In general, our analysis shows that the first two pilots performed well in the majority of viability issues. Below, we analyze each innovation pilot in greater depth.
Figure 2. Three pilot cases assessed with the viability radar
Electronic maternity card
The first case, an electronic maternity card, is currently piloted in one city and surrounding region in Finland. It involves replacing the traditional paper-based information storage procedures with an electronic health record service that allows expectant mothers online access all information relating to their pregnancy (Sitra, 2014). The objective is to improve the exchange of information among maternity clinics, expectant mothers, and hospitals to reduce the likelihood of mistakes, to improve customer service, and to make monitoring high-risk pregnancies more efficient. Besides self-monitoring their health, expectant mothers can use the electronic service to share information from their pregnancy with their family and friends if they so choose. The first innovation pilot passed all questions with the highest marks, except for concerns over adaptability of the innovation in other contexts with differing information systems and interoperability requirements. Adoption of the innovation may require heavy investments in electronic patient records by the service provider and can thus face challenges in wider diffusion at a time when public health care is looking for ways to cut spending, not increase it.
Medical care need assessment
In the second service pilot, the service provider's management team had a strong vision to speed up the triage process by replacing the phone interview with an electronic process in selected patient groups. This approach freed nurse resources for other tasks and encouraged some customers to seek care, which they would not have otherwise done so. It was demonstrated that carefully planned electronic procedures can be created, but a traditional phone interview was still required in some situations. The expansion of the innovation to new contexts requires integration and tailoring. Furthermore, the system provider of Case 2 needs to put forth more effort if it desires to expand adoption of the reform. The new practice has been accepted within its current special clientele. However, more efforts are expected if the innovation is to have wider societal consequences.
Senior tablet computer services
The third pilot focused on senior services. It demonstrated that a tablet computer is an acceptable and engaging platform for elderly people to receive health-related information and instructions – if the right content is provided. Technological execution was considered to be suitable for a wider adoption of the innovation, but the content and user guidance are the areas needing the most development efforts. The pilot had many severe issues to tackle, predicting failure of the reform. The business model especially was not successful and the parties did not see prospects for profitability or business growth. These challenges are crucial when the innovation is not substituting existing services. The participating organizations are not committed to the wider diffusion of innovation, as they lack mutual goals. Also, the current funding system is not supporting the idea becoming a market practices, as public and private partners are not interested in investing in preventive healthcare technology.
This study focuses on representing a practical tool for assessing the viability of service innovations in the healthcare context. Assessment of the cases indicates how the viability radar can be employed to understand the rich context of new technology. The template combines important issues that need to be considered in assessing the innovation’s diffusion potential. The radar is thus helpful in making funding decisions and in pivoting transformative service ideas.
This practical tool enabled us to focus on the most crucial questions relating to the institutionalization setting that surrounds the potential innovation analyzed in the empirical study. Thus, we were able to provide the funding institute with important information that often remains overlooked in decision making and ex-post analysis. Moreover, according to the discussion with the division head, nine months after the creation of the tool and first assessments, the template is now applied in the division: first, ex-ante in evaluating all potential innovation pilots to provide a basis for selecting the pilots that will be awarded funding, and then ex-post to give a final assessment of the pilots and to provide a basis for deciding on additional funding or other means of support. To conclude from the above, the empirical evidence validates our proposition: If managers want to select and advance the most viable innovations, then the viability radar will help them to identify and measure the viability of innovations and to analyze which innovation element(s) affect(s) viability.
It should be emphasized that a low rating of an innovation pilot in some parts of the viability radar does not simply translate as a “no go”. Instead, these ratings indicate the action points that require further attention from the managers if they want to push the innovation forward. If a new simplifying technology does not benefit from wide support, it is possible to influence other stakeholders in various ways. For instance, a demonstration can be developed to showcase the benefits of new technology. Second, opinions of authorities and other key stakeholders can be changed with active lobbying. Third, stakeholders may become more committed to the innovation diffusion if they participate in the development process.
As a scientific contribution, we continue the discussion on assessing the role of business models and value networks in the diffusion of innovations. Our study is an approach to operationalize the disruptive innovation (Christensen et al., 2009) template in healthcare, but it can also be contrasted with the discussion on “disruptive susceptibility” (Klenner et al., 2013), which focuses on the readiness of innovation networks to adopt new solutions. Similar to the study by Klenner and colleagues (2013), we extend the view of the value network from the service providers, customers, and competitors to more general market characteristics. Readiness for change is important, as regulations and institutions strongly affect not only private market characteristics but public service innovations. For instance, non-profit organizations always engage in maintenance or transformation of dominant institutional logic depending on whether it fits the actor’s aims or not. In line with Coule and Patmore (2013), we conclude that, in order to engage in deinstitutionalization or transformation of existing institutions, the service provider needs to have a viable business model with a value proposition that resonates with the aims of potential network partners.
The practical development and scientific approval of the developed template require further evidence. And, there is a need for a theoretically valid set of questions. The questions represented in this article are selected intuitively by consulting the related literature and the national funding institute whose main objective is promoting innovative projects aiming for sustainable well-being in Finland. Before the viability template is adopted into wider use, there is a need to ensure that all important questions are asked. Despite these remaining shortcomings, we believe that our study advances the assessment of the institutional setting that is still often overlooked in the general innovation literature.
Particularly, our study emphasizes the role of business models in networked environments in the healthcare context. The context is characterised by a separation of buyers (or financers) and users of innovations. This is an important notice that should be taken into account in assessing the generalizability of the template in other contexts. Therefore, we invite other scholars to test the tool, not only in the context of healthcare, but in institutional settings that represent more traditional business markets. We also invite them to enhance understanding of the quasi-market context in healthcare.
In the institutionalization process of transformative service innovations, we identify the importance of novel technology that outperforms existing solutions, innovative business models that are feasible to the partners, and an ecosystem consisting of supporting partners as well as regulations and standards supporting the diffusion of the innovation. We propose that, in order to transcend from service ideas to transformative service innovations, all or most of these elements need to be aligned during the innovation process.
We contribute to the service innovation and business model innovation research by explicating how to assess the viability of innovations. For practitioners (e.g., funding agencies, system providers, and business developers), we provide a set of concrete questions that may be addressed in evaluating and enhancing transformative service ideas. They are operationalized in the viability radar, which, in our empirical study, was shown to be usable in decisions over funding of innovation proposals, in recognizing elements of viability of an innovation demanding more attention, and in business development by increasing overall understanding of the potential barriers for diffusion in the wider institutional setting.
Last, we acknowledge that the development work of the practical template remains in its early stages. We intend to develop it further towards a practical business model innovation analysis tool, especially for small and medium-sized enterprises (SMEs). We also invite other scholars and practitioners to advance our understanding on how to assess the influence of institutional settings on the viability of transformative service innovations.
The authors also wish to thank Sitra for their collaboration in this study.
An earlier version of this article was presented at the 2014 Annual Conference of the European Association for Research on Services (RESER), and the authors are grateful for feedback from the conference participants and reviewers from both RESER and the TIM Review.
The work leading to these results has received funding from the Tekes "Design Thinking and Effectuation in Innovation Activities in Social and Health Sector" and "Data to Intelligence (D2I)" projects, and from the European Community’s Horizon 2020 Programme (2014–2020) under grant agreement 645791. The content herein reflects only the authors’ view. The European Commission is not responsible for any use that may be made of the information it contains.
Akaka, M., Vargo, S., & Lusch, R. 2013. The Complexity of Context: A Service Ecosystems Approach for International Marketing. Journal of International Marketing, 21(4): 1–20.
Anderson, J., Donnellan, B., & Hevner, A. 2012. Exploring the Relationship between Design Science Research and Innovation: A Case Study of Innovation at Chevron. In M. Helfert & B. Donnellan (Eds.), Practical Aspects of Design Science: 116-131. Berlin Heidelberg: Springer.
Baker, L., Birnbaum, H., Geppert, J., Mishol, D., & Moyneur, E. 2003. The Relationship Between Technology Availability and Health Care Spending. Health Affairs, Nov:W3–537–W3–551.
Baregheh, A., Rowley J., & Sambrook, S. 2009. Towards a Multidisciplinary Definition of Innovation. Management Decision, 47(8): 1323–1339.
Barnett, J., Vasileiou, K., Djemil, F., Brooks, L., & Young, T. 2011. Understanding Innovators' Experiences of Barriers and Facilitators in Implementation and Diffusion of Healthcare Service Innovations: A Qualitative Study. BMC Health Services Research, 11(1): 342.
Berwick, D. M. 2003. Disseminating Innovations in Health Care. JAMA, 289(15):1969–1975.
Busse, R., Blűmel, M., Scheller-Kreinsen, D., & Zentner, A. 2010. Tackling Chronic Disease in Europe: Strategies, Interventions and Challenges. Observatory Studies Series no 20. Geneva, Switzerland: World Health Organization.
Bouwman, H., De Vos, H., & Haaker, T. (Eds.) 2008. Mobile Service Innovation and Business Models. Berlin: Springer.
Burns, L. R., David, G., & Helmchen, L. A. 2011. Strategic Response by Providers to Specialty Hospitals, Ambulatory Surgery Centers, and Retail clinics. Population Health Management, 14(2): 69–77.
Caldwell, M., & Kleppe, I. 2010. Early Adopters in the Diffusion of an HIV/AIDS Public Health Innovation in a Developing Country. Advances in Consumer Research, 37: 326–332.
Carlson, R. J. 2009. The Disruptive Nature of Personalized Medicine Technologies: Implications for the Health Care System. Public Health Genomics, 12(3): 180–184.
Chesbrough, H., & Rosenbloom, R. 2002. The Role of the Business Model in Capturing Value from Innovation: Evidence from Xerox Corporation’s Technology Spinoff Companies. Industrial and Corporate Change, 11(3): 529–555.
Christensen, C. M. 1997. The Innovator’s Dilemma: The Revolutionary Book That Will Change the Way You Do Business. New York: Collins Business Essentials.
Christensen, C. M., Grossman, J. H., & Hwang, J. 2009. The Innovator's Prescription: A Disruptive Solution for Health Care. New York: McGraw-Hill.
Christensen, K., Doblhammer, G., Rau, R., & Vaupel, J.W. 2009. Ageing Populations: The Challenges Ahead. Lancet, 374(9696): 1196–1208.
Coule, T., & Patmore, B. 2013. Institutional Logics, Institutional Work, and Public Service Innovation in Non-Profit Organizations. Public Administration, 91(4): 980–997.
Currie, W. L., & Seddon, J. J. 2014. A Cross-National Analysis of eHealth in the European Union: Some Policy and Research Directions. Information & Management, 51(6): 783–797.
Danneels, E. 2004. Disruptive Technology Reconsidered: A Critique and Research Agenda. Journal of Product Innovation Management, 21(4): 246–258.
de Reuver, M., Bouwman, H., & Haaker, T. 2013. Business Model Roadmapping: A Practical Approach to Come from an Existing to a Desired Business Model. International Journal of Innovation Management, 17(1).
Dobrev, A., Jones, T., Stroetmann, V., Stroetmann, K., Vatter, Y., & Peng K. 2010. Interoperable eHealth Is Worth It: Securing Benefits from Electronic Health Records and ePrescribing. Bonn/Brussels: European Commission.
Dubosson-Torbay, M., Osterwalder, A., & Pigneur, Y. 2002. E-Business Model Design, Classification, and Measurements. Thunderbird International Business Review, 44(1): 5–23.
Dumay, J. C. 2010. A Critical Reflective Discourse of an Interventionist Research Project. Qualitative Research in Accounting & Management, 7(1): 46–70.
Dutta, S., & Bilbao-Osorio, B. 2012. The Global Information Technology Report 2012: Living in a Hyperconnected World. Geneva, Switzerland: World Economic Forum.
EFactors. 2003. E-Business Model Roadmap – E-Factors Report Part 1: Overview, and Current Trends on E-Business Models. Deliverable 3.1. IST-2001-34868. European Commission 1st Programme.
Fitzgerald, L., Ferlie, E., Wood, M., & Hawkins, C. 2002. Interlocking Interactions: The Diffusion of Innovations in Health Care. Human Relations, 55(12): 1429–1449.
Govindarajan, V., & Kopalle, P. K. 2006. The Usefulness of Measuring Disruptiveness of Innovations Ex Post in Making Ex Ante Predictions. Journal of Product Innovation Management, 23(1): 12–18.
Grady, J. 2014. CE: Telehealth: A Case Study in Disruptive Innovation. American Journal of Nursing, 114(4): 38–45.
Hahn, F., Jensen, S., & Tanev, S. 2014. Disruptive Innovation vs. Disruptive Technology: The Disruptive Potential of the Value Propositions of 3D Printing Technology Startups. Technology Innovation Management Review, 4(12): 27–36.
Hauser, J., Tellis, G., & Griffin, A. 2006. Research on Innovation: A Review and Agenda for Marketing Science. Marketing Science, 25(6): 687–717.
Heikkilä, M., & Heikkilä, J. 2013. Collaborative Business Model Innovation Process for Networked Services. In J. Järveläinen, H. Li, A-M Tuikka, & T. Kuusela (Eds.), Co-created Effective, Agile, and Trusted eServices. Lecture Notes in Business Information Processing, 155: 133–147. Berlin: Springer.
Heikkilä, J., Heikkilä, M., & Lehmonen, J. 2005. Sharing for Understanding and Doing for Learning: An Emerging Learning Business Network. IUP Journal of Knowledge Management, March 2005.
Heikkilä, M., & Kuivaniemi, L. 2012. Ecosystem Under Construction: An Action Research Study on Entrepreneurship in a Business Ecosystem. Technology Innovation Management Review, 2(6): 18–24.
Heikkilä, M., Solaimani, S., Soudunsaari, A., Hakanen, M., Kuivaniemi, L., & Suoranta, M. 2014. Performance Estimation of Networked Business Models: Case Study on a Finnish eHealth Service Project. Journal of Business Models, 2(1): 71-88.
Hevner, A. R. 2007. A Three Cycle View of Design Science Research. Scandinavian Journal of Information Systems, 19(2): Article 4.
Ho, D. 2007. Research, Innovation and Knowledge Management: The ICT Factor. Commissioned Paper for the UNESCO Forum on Higher Education, Research and Knowledge. Paris: UNESCO.
Iivari, J. 2007. A Paradigmatic Analysis of Information Systems as a Design Science. Scandinavian Journal of Information Systems, 19(2): Article 5.
Keller, A., & Hüsig, S. 2009. Ex Ante Identification of Disruptive Innovations in the Software Industry Applied to Web Applications: The Case of Microsoft's vs. Google's Office Applications. Technological Forecasting and Social Change, 76(8): 1044–1054.
King, N., & Anderson, N. 2002. Managing Innovation and Change: A Critical Guide for Organizations (2nd ed.). London: Thomson.
Kissinger, M. 2007. Retail Health Clinics Drive Innovation into Primary Care Practices. Journal of Medical Practice Management, 23(5): 314–319.
Klenner, P., Hüsig, S., & Dowling, M. 2013. Ex-Ante Evaluation of Disruptive Susceptibility in Established Value Networks: When Are Markets Ready for Disruptive Innovations? Research Policy, 42(4): 914–927.
Kraus, S., Pohjola, M., & Koponen, A. 2011. Innovation in Family Firms: An Empirical Analysis Linking Organizational and Managerial Innovation to Corporate Success. Review of Managerial Science, 6(3): 265–286.
Lanseng, E., & Andreassen, T. 2007. Electronic Healthcare: A Study of People’s Readiness and Attitude Toward Performing Self-Diagnosis. International Journal of Service Industry Management, 18(4): 394–417.
Lettl, C., Herstatt, C., & Gemuenden, H. G. 2006. Learning from Users for Radical Innovation. International Journal of Technology Management, 33(1): 25-45.
Leung, J., Chu, S. C., & Cheung, W. 2013. Design Research Guidelines for Mindful IT Innovations: The Case of RFID Innovation in Supply Chain Management. In Proceedings of the 46th Hawaii International Conference System Sciences (HICSS) Conference, 3727–3736.
Lukka, K., & Suomala, P. 2014. Relevant Interventionist Research: Balancing Three Intellectual Virtues. Accounting and Business Research, 44(2): 204–220.
Maglio, P. P., & Spohrer, J. 2013. A Service Science Perspective on Business Model Innovation. Industrial Marketing Management, 42(5): 665–670.
McColl-Kennedy, J. R., Vargo, S. L., Dagger, T. S., Sweeney, J. C., & Kasteren, Y. V. 2012. Health Care Customer Value Cocreation Practice Styles. Journal of Service Research, 15(4): 370–389.
Miller, R., & Sim, I. 2004. Physicians’ Use of Electronic Medical Records: Barriers and Solutions. Health Affairs, 23(2): 116–126.
Moore, J. 1993. Predators and Prey: A New Ecology of Competition. Harvard Business Review, May/June 1993.
Muegge, S. 2013. Platforms, Communities, and Business Ecosystems: Lessons Learned about Technology Entrepreneurship in an Interconnected World. Technology Innovation Management Review, 3(2): 5-15.
Nikayin, F., Heikkilä, M., de Reuver, M., & Solaimani, S. 2014. Workplace Primary Prevention Programmes Enabled by Information and Communication Technology. Technological Forecasting and Social Change, 89 (November): 326–332.
Oh, H., Rizo, C., Enkin, M., & Jadad, A. 2005. What Is eHealth (3): A Systematic Review of Published Definitions. Journal of Medical Internet Research, 7(1): 32–40.
Okazaki, S., & Castañeda, J. 2013. Physicians’ Appraisal of Mobile Health Monitoring. The Service Industries Journal, 33(13–14): 1326–1344.
Okazaki, S., Castañeda, J. A., Sanz, S., & Henseler, J. 2013. Physicians' Appraisal of Mobile Health Monitoring. The Service Industries Journal, 33(13–14): 1326–1344.
Ostrom, A. L., Bitner, M. J., Brown, S. W., Burkhard, K. A., Goul, M., Smith-Daniels, V., Demirkan, H., & Rabinovich, E. 2010. Moving Forward and Making a Difference: Research Priorities for the Science of Service. Journal of Service Research, 13(1): 4–36.
Rapoport, J., Chaulk, P., Kuropatwa, R., & Wright, M. 2011. Game Changing or Disruptive Innoavation: Analytical Framework and Background Study, Game Changing Innovations. Edmonton, Canada: Institute of Health Economics.
Rayna, T., & Striukova, L. 2014. The Impact of 3D Printing Technologies on Business Model Innovation. In P.-J. Benghozi et al. (Eds.), Digital Enterprise Design & Management: 119-132. Switzerland: Springer International Publishing.
Robert, G., Greenhalgh, T., MacFarlane, F., & Peacock, R. 2010. Adopting and Assimilating New Non-Pharmaceutical Technologies into Health care: A Systematic Review. Journal of Health Services Research & Policy, 15(4): 243–50.
Rogers, E. M. 1995. Diffusion of Innovations (4th ed.). New York: The Free Press.
Rohrbeck, R., Konnertz, L., & Knab, S. 2013. Collaborative Business Modelling for Systemic and Sustainability Innovations. International Journal of Technology Management, 63(1): 4–23.
Schulman, K. A., Vidal, A. V., & Ackerly, D. C. 2009. Personalized Medicine and Disruptive Innovation: Implications for Technology Assessment. Genetics in Medicine, 11(8): 577–581.
Sen, A. 2013. Totally Radical: From Transformative Research to Transformative Innovation. Science and Public Policy, 41(3): 344–358.
Seppälä, A., Nykänen, P., & Ruotsalainen, P. 2012. Development of Personal Wellness Information Model for Pervasive Healthcare. Journal of Computer Networks and Communications, 2012: Article ID 596749.
Shin, J. 2014. New Business Model Creation through the Triple Helix of Young Entrepreneurs, SNSs, and Smart Devices. International Journal of Technology Management, 66(4): 302–318.
Sitra. 2014. The Modern-Day Maternity Card. The Finnish Innovation Fund Sitra, March 17, 2014. Accessed April 22, 2015:
Tanev, S., & Frederiksen, M. 2014. Generative Innovation Practices, Customer Creativity, and the Adoption of New Technology Products. Technology Innovation Management Review, 4(2): 5-10.
Tellis, G. J. 2006. Disruptive Technology or Visionary Leadership? Journal of Product Innovation Management, 23(1): 34–38.
Thompson, V. 1965. Bureaucracy and Innovation. Administrative Science Quarterly, 10(1): 1–20.
Van Aken, J., & Romme, G. 2009. Reinventing the Future: Adding Design Science to the Repertoire of Organization and Management Studies. Organization Management Journal, 6(1): 5–12.
Vargo, S. L., & Lusch, R. F. 2008. Service-Dominant Logic: Continuing the Evolution. Journal of the Academy of Marketing Science, 36(1): 1–10.
Venable, J. R., Pries-Heje, J., Bunker, D., & Russo, N. L. 2010. Creation, Transfer, and Diffusion of Innovation in Organizations and Society: Information Systems Design Science Research for Human Benefit. In J. Pries-Heje, J. Venable, D. Bunker, N. L. Russo, & J. I. DeGross (Eds.), Human Benefit through the Diffusion of Information Systems Design Science Research: 1–10. Berlin: Springer.
Verschuren, P., & Hartog, R. 2005. Evaluation in Design-Oriented Research. Quality & Quantity, 39(6): 733–762.
Wade, J. E., Ledbetter, D. H., & Williams, M. S. 2014. Implementation of Genomic Medicine in a Health Care Delivery System: A Value Proposition? American Journal of Medical Genetics Part C: Seminars in Medical Genetics, 166(1): 112–116.
Wessel, M., & Christensen, C. M. 2012. Surviving Disruption. Harvard Business Review, 90(12): 56–64.
WHO. 2015a. Health Topics: Ageing. World Health Organization. Accessed April 23, 2015:
WHO. 2015b. Health Topics: Obesity. World Health Organization. Accessed April 23, 2015:
Yin, R. 2004. Case Study Research: Design and Methods. Thousand Oaks, CA: Sage Publications.
Keywords: business model, ecosystem, healthcare, innovation, institutionalization, technology, transformative service, viability, viability assessment, viability radar