Acknowledgements
Kempers, D. 1 Student info: Students № 10671560/ 30-04-2014
Msc Business Studies - Strategy Track /UvA Supervisor: René Bohnsack /Faculty of Economics and Business
devices
Prevention as The New Cure
Successful market deployment of Preventive eHealth
By Daan Kempers
Abstract:
This paper proposes market opportunities, communication channels and product features that support the diffusion process of preventive eHealth innovations toward financial success. Healthcare systems in the Western Societies are deemed to innovative slow and move towards quality, demand and cost issues. eHealth and preventive innovations hold the promise to overcome these rising pressures, but many such products fail due to ineffective strategies in overcoming acceptance and usage issues. Furthermore, the nature of prevention is described to be an additional factor that hampers product embedding. This research investigates the market opportunities for preventive eHealth by deploying and testing insights from the Disruptive Innovation Theory. The core of the research explores the embedding of preventive eHealth solutions in the light of innovation diffusion models. Findings of this paper suggest the need for the inclusion of powerful sales partners,
communication channels and a number of features to stimulate preventive behavior in business models. These findings can support managers in grasping the market opportunities for preventive eHealth.
Acknowledgements
The topics of innovative eHealth business models and innovation in the general healthcare industry have grasped my interest and full motivation over the course of the thesis process. The people that I could work with and the extensive literature on the subject have been a huge influence on my interest in the subject and extension of my knowledge base. For this, and the resulting thesis I would like to express my gratitude to several individuals who have supported me throughout the thesis process.
For the initiation of the research and it’s topic I would like to thank René Bohnsack, my thesis supervisor. Dr. Bohnsack made me aware of the issues surrounding eHealth embedding and implementation, and thereby raised my interest on the subject. Furthermore, I would like to thank Dr. Bohnsack for his dedication, support and knowledge which he showed and shared throughout the thesis process.
Secondly, I would like to express my gratefulness to all the respondents that were willing to share their expertise as data for this research. These entrepreneurs, consultants, healthcare practitioners, academics and IT specialists have shown great openness, interest and collaboration. It was thanks to their insights on the topic of eHealth diffusion that this paper could be established.
Lastly I would like to thank the many fellow students, my family and friends that supported and assisted me in the process of writing this thesis.
Table of contents
- Acknowledgements
2- Table of contents
3- Index of Tables & Figures
5- 1. Introduction
6- 2. Literature Review
82.1 Preventive eHealth Innovations 8
2.1.1 The Innovativeness of Preventive eHealth 9 2.2 A Theory of Disruptive Innovations and eHealth 11
2.2.1. Development of The Disruptive Innovation Theory 11
2.2.2 Essential Insights to a Theory of Disruption 12
2.2.3 Market Disruption Opportunities for eHealth 13
2.3 An Innovation Diffusion Perspective on eHealth 15
2.3.1 Issues of Innovation Diffusion 15 2.3.2 Theories of Innovation Diffusion and eHealth 17
2.3.3 Essential Insights to a Theory of Innovation Diffusion 18
2.3.4 An Organizational Perspective on Innovation Diffusion 19 2.3.5 A Psychology Perspective on Innovation Diffusion 20 2.3.6. A Communication and Knowledge Perspective on Innovation Diffusion 21 2.3.7. Diffusion and Adoption of Preventive Innovations 21
- 3. Conceptual Framework
24- 4. Research Methodology
29 4.1Research Strategy 29 4.2 Scientific Approach 294.3 Research Methods 30 4.4 Data Sample and Collection 31 4.5 Interviews 34 4.6 Respondents 35
4.7 Reliability, Replicability and Validity 36
- 5. Results
385.1 The Independent Variable 38
5.1.2. Definitions of Prevention 39
5.2 Market Opportunities 39
5.2.1 Commercial Markets 41
5.3 Communication Channels and Sales Partners 43
5.3.1 Healthcare Professionals 43
5.3.2 Insurance Companies 43
5.3.3 National Governments 48
5.3.4 IT Incumbents 49
5.4 Features Stimulating the Adoption of Preventive eHealth Technologies 50
5.4.1 Convenience 50
5.4.2 Feedback Systems and Consciousness 52
5.4.3 Fun and Social Aspects 53
5.4.4 Ease of Use 52
5.5 Future Outlook 56
- 6. Discussion
59- 7. Limitations
65- 8. Suggestions for Further Research
66- 9. Conclusion
66- 10. References
67- 11. Appendix
72Index of Tables & Figures
Index of Tables
Table 1: An overview of the barriers to innovation 16
Table 2: Quantified overview of the levels of expertise amongst respondents 33
Table 3: eHealth definition per expert 39
Index of Figures
Figure 1: Disruption Types 10
Figure 2: Performance trajectories of disrupting vs. sustaining innovations 13 Figure 3: Visualization of the innovation diffusion process 19 Figure 4: The trajectories of complexity levels of diagnosis and treatment 25
Figure 5: The conceptual framework 28
Figure 6: The Gartner Hypecycle 41
Figure 7: Market deployment strategies, supply considerations 47 Figure 8: Market deployment strategies, demand considerations 56
1. Introduction
At the dawn of the 21st century, Clayton Christensen, Richard Bohmer and John Kenagy (2000) referred to the healthcare industry as ‘’diseased’’ and wandered if disruptive innovations could provide the cure. The metaphorical disease refers to the industries slow response to major challenges of increased demand, rising efficiency- and cost pressures, dealing with limited personnel and the request for better access (Christensen & Overdorf, 2000; Koch, 2006). Perhaps not entirely coincidently, the paper was written in a time in which information technology and electronic communication based products and services for healthcare application spotted its first serious opportunities. These type of innovations now go under the combined name of eHealth technologies and have been recognized and acknowledged by academics for holding the promise to overcome the threats facing the healthcare industry (Ahern, 2007; Kreps & Neuhauser, 2010; Oh, Rizo, Enkin, & Jadad, 2005). Yet simultaneously it is to be conceded that most of the population has used their computer for not more than a sporadic ‘’symptom search’’ on Google.com and a rough 75% of all eHealth businesses fail during the operational stage
(Anderson, 2004; Chen, Cheng, & Mehta, 2013). Hence one can conclude that though demand is thriving and technologies are ready, the challenges for diffusion of eHeath innovations are greater. The industry is need of guidance on these matters before success can be achieved.
The challenges decelerating the pace of eHealth diffusion are numerous and the process is nourished in its complexity by the high amount of stakeholders (Herzlinger, 2006; Kreps & Neuhauser, 2010). The current healthcare industry is often labeled one of the most change-averse industries, entrenched by the dominance and interests of insurance companies, profit- and non-profit institutions, governments and professionals (Christensen, Hwang, & Grossman, 2008; Christensen & Overdorf, 2000; Herzlinger, 2006). This is partially the reason that many
academics, including Christensen (2000), expect a radical movement of health solutions towards self-care and prevention in the everyday life (Christensen et al., 2008; Herzlinger, 2006; Rogers, 2002). However, prevention and self-care present major additional challenges for the embedding and adoption of eHealth solutions (Overstreet, Cegielski, & Hall, 2013). The absence of the professional, the intangible outcome and the required effort are just a part of the aspects of prevention that hamper adoption (Rogers, 2002). The support for entrepreneurs to open the presumed window of opportunity and move towards mass market adoption requires strategic guidance and insights where the chances lie and how barriers can be overcome (Ahern, 2007; Kreps & Neuhauser, 2010). These ongoing issues raise the necessity to research the question: How can preventive eHealth innovations reach sustainable financial performance?
In response to the unfulfilled promise of eHealth commercialization and diffusion, the academic literature massively engaged in exploratory search, finding numerous barriers and
challenges in the way of success (e.g. Ahern, 2007; Black et al., 2011; Kreps & Neuhauser, 2010). Mainly focused on market deployment issues, a wide set of social, ethical, legal, financial,
technological and organizational barriers emerged as the result. These resolutions, together with the apparent failure rate of many eHealth initiatives increased the need for descriptive research on the drivers behind business models that were able to succeed (e.g. Broens et al., 2007; Chen et al., 2013). Explanatory research forms a third distinctive academic section on healthcare innovations, and has examined the current stance of the industry through the lens of the Theory of Disruption and the Theory of Innovation Diffusion (e.g. Christensen, Bohmer, & Kenagy, 2000; Christensen et al., 2008; May, 2013; Tanriverdi & Iacono, 1999). The latter has thus far been applied in multiple influential papers to gain an understanding of embedding of Telemedicine technologies, a specific section of eHealth covering IT applications in providing clinical health. Consecutive papers progressed the theory from measuring organizational readiness, towards modeling
physician acceptance of technology (Hu, Chau, Sheng, Tam, & Sheng, 1999; Jennett, Yeo, Pauls, & Graham, 2003). However, these papers focus on large healthcare organizations and work within the existing institutional frameworks, thereby offering few insights for more radical breakthrough eHealth innovations that potentially restructure the industries landscape. The earlier mentioned theory of disruptive innovation does seek to answer innovative entrepreneurs can overcome major challenges through higher quality and lower price (Christensen & Bower, 1995). This theory however only describes the characteristics of disruptive innovations, and therefore it only holds a small part of the answers to successful innovation diffusion.
The purpose of this paper is to derive insights for market deployment strategies of preventive eHealth, presented as managerial implications. The literature review describes both predictions and challenges for preventive eHealth innovations through the lens of the Innovation Diffusion Theory and the Disruptive Innovation theory. From the review a conceptual framework of strategic considerations for successfully market deployment of preventive eHealth is derived. The framework serves as a theoretical basis to a set of semi-structured interviews that are conducted amongst field experts and prominent stakeholders. The results and discussion are designed and intended to produce answers to how eHealth entrepreneurs can disrupt the
healthcare industry. Till this day many eHealth businesses have attempted and failed to cure our diseased healthcare industry. Still many academics mention its extensive range of opportunities (e.g. Stanberry, 2000; Koch, 2005; Chen et.al., 2013). The question is not if preventive eHealth holds the innovative capacity to cure the industry, but how it can do so.
2. Literature Review
For the establishment of a proper and valuable research methodology multiple theories and papers that are relevant to the context of preventive eHealth innovations are reviewed. The first section covers the definition and explanation of the core concepts in the research. This aim of these paragraphs is to clarify the subject and prevent the occurrence of misinterpretations. The second section explicates the theory of Disruptive Innovations and its applications to the healthcare environment. These academics predict and identify market opportunities for healthcare
innovations, based on characteristics of past disruptive innovations. The final and core section of the literature review elucidates on theories of innovation diffusion. These theories provide insights into embedding processes of innovations, and can hence be deployed to investigate how
opportunities for Preventive eHealth solutions can be turned into successful products.
2.1 Preventive eHealth Innovations
As stated in the introduction, ehealth is the combined name for any product or service that deploys electronic communication or information technologies for delivering health related implementations (Kreps & Neuhauser, 2010; Oh et al., 2005). Though different definitions are thus far being used throughout the literature, most experts agree that it includes an IT and/or electronic communication component. Due to the comprehensiveness of this concept and its broad applicability, there is no singe innovation categorization that applies to all eHealth. From a health delivery perspective there are three levels on which eHealth applications can operate. The first level is the post-diagnosis level, on which the innovation operates to cure or to manage a chronic disease. Most telemedicine applications are designed to replace conventional solutions on this level of clinical and medical health (Jennett et al., 2003; Tanriverdi & Iacono, 1999). A second level is that of diagnosing, this is involves online symptom searches, online consultations, or peer diagnosing networks. Academics argue that on this level eHealth can lower the need for physical attendance, for medical checks or even take away the role of the ‘’expensive’’ doctor (Kreps & Neuhauser, 2010; Parente, 2000). A third and final level for which eHealth offers solutions is the pre-diagnosis level, also referred to as preventive medicine.
Preventive health innovations are services or products that are aimed at avoiding having to cure at all. In the healthcare spheres this is achieved by the support of maintaining a healthy lifestyle. eHealth innovations on this level are of most radical nature in the sense that they address a market that has been left relatively untainted (Norman & Skinner, 2006). Recent literature suggests that the adoption processes of preventive innovations differ significantly from traditional innovations (Overstreet et al., 2013). This is the case since the benefits/outcome is often
preventive innovations requires special attention and is presumed to move relatively slow. Rogers (2010) points out, in the light of the Theory of Innovation Diffusion, that the earlier mentioned reasons cause the perceived relative advantage of products that include a component of prevention to be low. Hence, it is suggested that entrepreneurs pay additional attention to developing and communicating relative advantages in order to achieve the expected levels of adoption (Rogers, 2010). When combined, the unique characteristics of preventive innovations and the newness of eHealth bring forth many challenges for the diffusion of such products. The aim of this paper is to find and test potential strategic solutions for managers to implement when introducing preventive eHealth innovations.
2.1.1 The Innovativeness of Preventive eHealth
Before looking at diffusion processes, it is essential to understand the theoretical nature of the innovation itself, often labelled as the level of ‘’newness’’ or ‘’innovativeness’’ (Downs & Mohr, 1976; Garcia & Calantone, 2002). This categorization is important since the degree of
innovativeness is likely to influence the diffusion process (Rogers, 2010). Multiple typologies co-exist and distinguish innovations based upon different dimensions that represent these degrees of innovativeness. A clear understanding of innovation newness on different levels helps to see where the difference from previous products, services or methods is so large that it causes difficulties for transformation and implementation (Dewar & Dutton, 1986; Garcia & Calantone, 2002). The earlier mentioned classification into radical and incremental innovations is one of the first and most widely used distinctions (Daft, 1978; Dewar & Dutton, 1986; Garcia & Calantone, 2002). However, more recent work has questioned whether this typology covers all relevant aspects and added different perspectives. Garcia & Calantone (2002) added a third type, as being ‘’really new’’ innovations that serves to describe the section between the two extremes.
When discussing whether innovation is incremental, really new, radical or discontinues it is important to note that this depends on the perspective one takes. An innovation can for example present some radically new aspects to a consumer, while the technology is well known to the supplier (Garcia & Calantone, 2002). Relevant to this paper are the industry and macro level perspectives towards successful market deployment strategies. An innovation is defined as a radical one by Garcia and Caltone (2002) when it is new to the industry in terms of both technology and marketing. Preventive eHealth devices rely on computerized technologies that attempt to influence ones behavior by the use of standardized or feedback information and exercises. Feedback systems are designed to detect behavior for analysis and possible
correction/behavior change (Milenković, Otto, & Jovanov, 2006; Parente, 2000). Computerized technologies, such as sensors and camera’s for the use of monitoring behavior are often in the R&D phase and not widely incorporated in the industry for health devices. These technologies are
still to be developed for proper use of the purpose they are to serve. Furthermore, these technologies are also radically new to the market of potential consumers. Preventive feedback devices are being deployed in some workplace environments, however these do often not rely on advanced technologies for real time response and merely serve as productiveness surveillance (Maxim, Allshouse, Kelly, Walters, & Waugh, 1997; Nord, Mccubbins, & Nord, 2006). In this line of reasoning, the use of advanced information technology and computer communication technologies that serve to monitor the behavior of an individual matches the description of being radically new to the potential market.
It is often the case that preventive eHealth is not only new to the market, but the markets for preventive products are also relatively undefined or limited in size. For this reason Christensen & Raynor (2003) distinguish between ‘’low end disruptions’’ and ‘’new market disruptions’’ as is presented by figure 1. ‘Low end disruptions target the consumers who do not need the full
performance that incumbents offer to serve high end consumers. This type matches most eHealth solutions that operate on a post-diagnosis, and diagnosis level that are aimed at reducing costs and increasing convenience (Anderson, 2006; Black et al., 2011; Christensen & Raynor, 2003). A second type, more relevant to preventive solutions, is that of ‘’new market disruptions,’’ which operate in a section that has previously not been served by healthcare incumbents (Christensen & Raynor, 2003). Christensen et.al. (2000) state that incumbents have focused on enhancing the quality of medical and clinical services, while overlooking the potential market for (e)Health implementations of preventing their patients from getting the ‘’disease’’ in the first place (Christensen et al., 2000). The use of eHealth to
stimulate preventive behavior by individuals in the workplace or home environment taps into this thus far relatively new and developing market. The following section will further explicate what market opportunities have been identified and how the level of newness affects diffusion processes.
2.2 A Theory of Disruptive Innovations and eHealth
In the search for how preventive eHealth can successfully enter the healthcare market, the first question that arises is whether the market is need of eHealth innovations at all. The theory referred to as ‘’disruptive innovation theory’’ points out to fulfill a major role in finding that answer. This theory studies the emergence of the more radical changes in the business
environment that have the potential to overthrow established products and services (Christensen & Bower, 1995). In the specific case of preventive eHealth applications, the theory on how
disruptive innovations come about forms the foundation for instrumental and normative
reasoning. By this is meant that lessons drawn from past disrupting technologies can be modelled into strategic guiding principles on market deployment strategies. However, it will show that the theory only supplies a general basis of knowledge, and hence requires context specific research to reach managerial implications for market deployment strategies. The following section describes the basics of the disruptive innovation theory, presents how the theory has been related to the healthcare industry and covers the possible link to e-health solutions.
2.2.1 Development of the Disruptive Innovation Theory
The foundations for a disruptive innovation theory date back to 1995, when Bower & Christensen noticed how incumbent firms often fail to retain their dominant positions when technologies or markets change. The theory was first established from the incumbent perspective, indicating what internal factors causes firms to fail or lack transformative capacities in the process of disruptive change (Christensen & Bower, 1995; Christensen & Overdorf, 2000; Christensen, 1997). The early work took a demand perspective based on the notion that mass market
deployment of innovations is often accomplished by new entrants, claiming that incumbents have a tendency to overshoot the consumer demand with their sustained innovations (Christensen, 1997). More valuable however for enhancing our understanding of eHealth potential are the later additions to the theory. These take the opposite point of view and worked towards uncovering how disruptive innovations come about, and the struggles that these processes incur (Christensen, 2006). Example papers from this perspective include ‘’the innovators prescription’’ (Christensen et al., 2008) and ‘’the ongoing process of building a theory of disruption’’ (Christensen, 2006). Even more relevant to this research are a number of scientific articles that have specifically related and applied the disruptive innovation theory to the healthcare industry (i.e. Christensen, Bohmer, & Kenagy, 2000; Christensen et al., 2008; Herzlinger, 2006). The problems that this section of the literature addressed resolve around the industries failure to overcome issues of high costs and limited access under the conditions of an ever growing and ageing population
‘’the most entrenched, change averse industry in the U.S.A.’’ From this point of view, disruptive innovations in both technologies and business models are ought to ‘’cure the industry’’ and improve the access for the entire population.
2.2.2 Essential Insights to a Theory of Disruption
Understanding whether, which and how e-Health applications can disrupt the industry requires a dissection of the aspects and features of a disruptive innovation. The disruptive innovation theory provides a theoretical understanding of how industry shifts/transformations occur through emergence business model or technological innovations. Though Christensen and Bower (1995) are clear on the aspects of phenomenon that the theory describes, one could criticize the lack of a specified definition (Danneels, 2004; Markides, 2006). This has left follow up research with the necessity to individually establish a definition of the concept, resulting in diverging answers. A general definition used across literature describes an innovation as the creation and/or application of novel ideas, knowledge, methods, skills and behaviors (Christensen & Bower, 1995; Daft, 1978). Schumpeter (1934)defined the concept in the business management sphere as ‘’the introduction of new goods, production methods, new supply sources, new
organizations or the opening of new markets.’’ An innovation gains the characteristic of disruptiveness when it entails the capacity to literally disrupt the market through replacing the established dominant product, business model or business processes. (Christensen & Bower, 1995).
The beginning of a transition process is typically phrased as an innovation entering the market from ‘’below.’’ By this is meant that disruptive technologies tend to be adopted first by the low end of the market due to their characteristics of convenience, simplicity and low costs. At this time the mass market is served by incumbents and their sustaining products or technologies. Sustaining products are defined as the dominant product/service offering, aimed at the high end of the market and advanced through years of incremental adjustments (Christensen et al., 2000; Christensen & Bower, 1995; Christensen & Overdorf, 2000). The theory is built on multiple examples of the past and claims that the sustaining products will eventually overshoot the consumer demand and capacity, creating a window of opportunity for disruptive technologies to replace them. Disruptive technologies, such as the home printer or mini-computer, quite often go through a struggle of finding demand and in this process create new markets for themselves. The theory argues that this is a major reason for incumbents to dismiss such opportunities early on and find themselves to be overthrown a few year later (Christensen et al., 2000; Christensen & Bower, 1995). The process of transition and replacement is presented in figure 2.
though consumers might perceive a product as simpler, the underlying technology needed to achieve this is likely to be more complex. Other characteristics that define a disruptive innovation are high levels of perceived radicalness (i.e. degree of newness to the consumer), perceived convenience opposed to the previous innovations, and it involves lower costs than previous innovations. An often used example of such a disrupting innovation is that of the replacement of analogue cameras by digital cameras, which allowed for easier access to- and selection of- photo’s (Lucas & Goh, 2009). However, one can question whether these cycles and processes always apply to a disruption of the market. Critics doubt to some extent the generalizability of such a theory and suggest the need for more empirical evidence or more industry and innovation specific implications (Danneels, 2004; Markides, 2006).
Figure 2 Performance trajectories of disrupting vs. sustaining innovations (Christensen, 2000)
This paper acknowledges the value of the disruptive innovation theory in explaining market transitions, but simultaneously keeps the above mentioned critiques in mind. Hence, the disruptive innovation theory will be employed as a supportive basis, and added with context specific insights from the eHealth market for preventive devices. This answers partially to the request of Danneels (2004) and Markides (2006) for an examination of the theories assertions in different markets and situations. The results will be discussed in the light of the disruptive innovation theory in an attempt to extend its managerial implication.
2.2.3 Market disruption opportunities for eHealth
theory in specific, have been applied numerous times to address the issues in the healthcare industry (Christensen, Baumann, Ruggles, & Sadtler, 2006; Christensen et al., 2000; Herzlinger, 2006; Hwang & Christensen, 2008). These papers mainly deploy a macro-economic analysis on the challenges these writers assert to be threatening the healthcare systems of the western and developing societies (Christensen et al., 2006). Thus far, the literature has presented and analyzed two major questions: can disruptive innovations overcome the below listed challenges to the sustainability of our healthcare system, and what has prevented such innovations and technologies from emerging earlier on? (Christensen et al., 2000; Herzlinger, 2006). The literature presents and is founded on four major interrelated challenges that the healthcare industry is currently facing:
- Healthcare delivery is increasingly becoming more expensive and is deemed to lack in overall cost effectiveness (Christensen et al., 2006, 2000). Academics have presented ‘’shocking’’ systematic increases of the healthcare share in the U.S. gross domestic product (GDP), now adding up to more than one sixth (Hwang & Christensen, 2008). Though a large market for healthcare is not a problem in itself, academics relate the growth to ineffectiveness of operations and misallocation of finances. The presumed lack of innovation in the industry causes professionals and patients to use outdated, time consuming and expensive methods, while changes in allocation of investments and research might allow for more cost effective solutions (Christensen et al., 2000; Herzlinger, 2006).
- Two separate forces cause steep growth rates in the expected demand for healthcare in the upcoming decades. One of which is population growth, which is mainly posing a threat on developing nations since predictions present stagnating growth rates in Western Societies (Christensen et al., 2008). However, a second force referred to as ‘’the ageing of the population’’ constitutes major pressures on both the Western civilization and developing nations (Zweifel, Felder, & Meiers, 1999). The latter is caused partially by the high birth rates after WOII. Ironically, a second cause are the technological innovations and
knowledge advancements of the healthcare industry itself which increase life expectancy and thus increase the percentage of elderly persons in these societies (Christensen et al., 2006).
- The increase in demand and costs of healthcare will put further pressure on the third major challenge which this paper categorizes as access. Access relates to the distribution of healthcare which presents rising inequalities from waiting lists, escalating costs for the poorer half and a decrease in small/local facilities (Ahern, 2007; Herzlinger, 2006; Hwang & Christensen, 2008). Similar to cost effectiveness, technological innovations such as
internet based chronic disease management present possible solutions that have yet to be deployed on a large scale (Ahern, 2007; Black et al., 2011)
- Though the last challenge of retaining healthcare quality is embedded in all of the previous three, it is often treated as a distinctive result which requires separate attention (Ahern, 2007; Black et al., 2011; Christensen et al., 2000; Hwang & Christensen, 2008). Currently academics fear for the quality assurance in the healthcare industry and have explored examples of how it is affected by growth in demand, cost/effectiveness issues and access limitations (Black et al., 2011; Christensen et al., 2000). If the industry is not able to change through business model or product innovations, the quality of healthcare, explained as keeping as many people as healthy as possible, will suffer (Black et al., 2011; Herzlinger, 2006).
As stated before, the above mentioned challenges are the combined reason for the application of a disruptive innovation theory to the context of the healthcare industry. The relevance of this link is founded on the progress with regard to increased access, higher quality and lower costs that previous innovative disruptions introduced in their markets (Christensen et al., 2006; Christensen & Bower, 1995; Hwang & Christensen, 2008). It is thus claimed that through innovations these challenges can be overcome. From a theoretical perspective academics argue that mass market deployment of these innovations will therefore occur through a process of disruption, aiming at the least demanding consumers (Christensen et al., 2000). Academics suggest possibilities in business model innovations, technological product innovations or a combination of the two (Christensen et al., 2000; Hwang & Christensen, 2008). These provide possible strategic insights to eHealth solutions and preventive applications in specific, which are discussed in section 1.3. First however it is important to note that the related research has taken an extensive look at the question preceding or supporting ‘’how to disrupt the market?’’, namely that of ‘’why has it not been done yet?’’ Answers on these questions can be found in the literature on Innovation
Diffusion (Rogers, 2010). These can be used to explain the barriers and strategic solutions in the process of innovation adoption.
2.3 An Innovation Diffusion Perspective on eHealth
2.3.1 Issues of Innovation Diffusion
Though the theory of disruption clearly indicates the demand for innovations, one can simultaneously conclude that somehow the supply is lacking behind or failing to fill the gap. Over the last decade a multitude of scientific articles have been dedicated specifically-, or partially to uncovering the barriers that stand in the way of healthcare innovations (Black et al., 2011; Broens
et al., 2007; Herzlinger, 2006). These barriers have been addressed especially by eHealth research, a field of the literature that which has presented numerous computer and internet based solutions. However simultaneously they are the most prominent in acknowledging the fact that most eHealth ventures, programs and initiatives fail to sustain. It is estimated that up to 75% of all eHealth programs fail during the operational stage (Chen et al., 2013). Table 1 presents an overview of the barriers to innovation as indicated by e-health research and the related disruptive innovation literature.
Barriers Pooled definition Literature Financing The lack in financial support from healthcare institutions
and the commercial markets to stimulate eHealth R&D, the generation of profitable business models and commercialization of high fixed cost innovations.
(Baumann, Ruggles, & Sadtler, 2006; Broens et al., 2007; Herzlinger, 2006; Hwang & Christensen, 2008;
Tanriverdi & Iacono, 1999) Policy and Legislation The combined lack in support and initiative of
governmental institutions for providing a favorable environment to internet and computer based innovations in healthcare.
(Anderson, 2006; Broens et al., 2007; Herzlinger, 2006; Stanberry, 2000)
Technology The technological challenge of creating user friendly products and services that are both more effective and efficient than current solutions.
(Black et al., 2011; Broens et al., 2007; Herzlinger, 2006; Koch, 2006; Kreps & Neuhauser, 2010)
Players/stakeholders Powerful stakeholders such as insurance companies, healthcare institutions and the drug industry that can hinder successful commercialization of eHealth applications that threaten them with regard to market share and profits
(Ahern, 2007; Baumann et al., 2006; Christensen et al., 2000, 2008; Herzlinger, 2006; Koch, 2006)
Consumer acceptance The combination of factors that hinder the mass market adoption of eHealth by consumers. This includes knowledge barriers, ethical issues, negative quality perceptions and negative safety perceptions.
(Anderson, 2006; Black et al., 2011; Broens et al., 2007; Herzlinger, 2006; Kreps & Neuhauser, 2010; Stanberry, 2000; Tanriverdi & Iacono, 1999; Wilson & Lankton, 2004) Organization acceptance The combination of factors that retain the adoption of
innovations and eHealth applications in by professionals. This includes knowledge barriers, change aversion, quality bias and organizational readiness.
(Ahern, 2007; Broens et al., 2007; Christensen et al., 2000; Hu et al., 1999; Hwang & Christensen, 2008; Jennett et al., 2003)
Table 1 An overview of the barriers to innovation
Though much of the above mentioned research is eHealth focused, the table represents barriers to innovations in the healthcare industry in general. It is to be noted that computer and electronic based innovations, under the combined name of eHealth, do make up the biggest share
of current R&D initiatives (Ahern, 2007; Chen et al., 2013; Hardey, 2001).
The mentioned barriers have been widely explored and acknowledged across literature. This process of exploration has mainly evolved on a macro level view. Often these papers
generalize across multiple types of eHealth while explaining the market failures (e.g. Baumann et al., 2006; Chen et al., 2013; Christensen et al., 2000; Herzlinger, 2006). In reality however, eHealth products and services are not as generalizable as the literature suggests. It can be
perceived a concept describing a manifold of different computer based technologies that allow for both product and business model innovations (Black et al., 2011; Chen et al., 2013; Herzlinger, 2006). Hence, for market deployment strategies it is a necessity to look a context-, product-, and business model specific factors that might influence successful implementation. Not all barriers to innovation apply to preventive eHealth, and some barriers that are specific to such an innovation might be overlooked by the general literature. Therefore the following section is an analysis on the current literatures contribution on market deployment strategies
It can be concluded that the insights of the disruptive innovation theory present that there is need for such innovations in healthcare industry. Also, it partially explains why these
disruptions have not occurred yet. Hence, this suggests that there is a significant demand which is not being served effectively. The problems mentioned are present on a macro level, while the research on market deployment strategies requires a more in depth look on specific product designs and marketing issues. Therefore the focus of this paper lies with the question of how the barriers for general deployment of eHealth relate to the context of preventive eHealth. The next section looks more specifically into literature findings on preventive innovations.
2.3.2 Theories of Innovation Diffusion and eHealth
Everett M. Rogers first work on the diffusion of innovations dates back to 1962, and over time has been adapted to become a leading theory for describing the emergence of innovations (Rogers, 2010). The objective of the theory is to explain and understand the process by which innovations are adopted by-, and embedded in- societies of mass consumption. Diffusion is the process by which an innovation is communicated through certain channels over time amongst members of a social system (Rogers, 2010). Due to his background in sociology and marketing Rogers was able to establish and describe both strategic matters and the underlying consumer psychology that play part in each phase of the innovations life-cycle. For this reason it has been extended and deployed in a variety of disciplines including marketing, strategy, psychology and sociology (Rogers, 2010). The large amount of reported innovation adoption issues in and surrounding the healthcare industry have raised the interest in the application of innovation theories. Over the years many academics have deemed the basics of the diffusion theory as especially valuable and applicable to understanding the embedding of innovations in the healthcare industry (e.g. Fitzgerald, Ferlie,
Wood, & Hawkins, 2002; Greenhalgh, Robert, MacFarlane, Bate, & Kyriakidou, 2004; Jennett et al., 2003; May, 2013; Tanriverdi & Iacono, 1999).
2.3.3 Essential Insights to a Theory of Innovation Diffusion
Diffusion consists of four components that influence adoption. The first of those is the consumer perception of the characteristics of the innovation (Rogers, 2010). This paper looks at innovations that are embedded in a product or service, and hence describes them as such. The perceived characteristics of an innovation are defined as relative advantage, compatibility, complexity, triability and observability. Relative advantage and compatibility involve conscious and
unconscious comparisons to needs and experiences as shaped by previous solutions. Complexity describes the perception of the level of difficulty to understand and use the innovation. Triability explains to what degree the potential users are able to experiment with the innovation without actual purchasing. Lastly, observability covers the degree to which the outcomes of the innovation are visible to the user and others (Rogers, 2010). Current eHealth literature has mostly described issues surrounding the perception of relative advantage, the degree of compatibility and levels of complexity (i.e. Herzlinger, 2006; Hu, Chau, Sheng, Tam, & Sheng, 1999; Tanriverdi & Iacono, 1999; Wilson & Lankton, 2004).
A second component is that of communication channels. This covers the medium and means by which individuals are made aware of the product and its claims of credibility. The theory states that higher levels of heterophily (i.e. differences in education, beliefs, social status etc.) amongst the communicators drives adoption (Rogers, 2010). Due to the medical component in eHealth products, these innovations often move solely through channels of general healthcare practitioners. Many academics have pointed out that several underlying reasons cause these practitioners to slow down the adoption process, in accordance with the theory (i.e. Broens et al., 2007; Fitzgerald et al., 2002; Hu et al., 1999).
Rogers (2010) frames the third elements as that of time to indicate the process by which an individual makes a decision. This five step process starts with the knowledge or awareness of the product/innovation. Over time, while knowledge is gained, an individual will form an attitude towards the innovation after which a decision is made to reject or adopt the product. Adoption leads towards the last two steps of the process, implementation of the idea and confirmation of the decision that was made. This process of adoption is founded on the idea that social interaction shapes all steps in the process. M. Rogers (2010) acknowledges that purchasing decisions are not solely an individual process, but merely a process in a society at large. Though eHealth has thus far often been researched within the healthcare structures, the underlying sociology insights in the diffusion theory form a valuable basis for market deployment strategies in eHealth’s potential
business to consumer markets (Ahern, 2007).
The last element to the model is that of the social system by which attitudes and behaviors are presumably shaped. The social system is a structure of norms which establish behavior
patterns. In the business spheres multiple actors, both profit and non-profit, attempt to influence these norms. Examples of such as actors are opinion leaders, which are influential actors on informal levels. Change agents are those that attempt to change behaviors to what they consider desirable. Together with other actors in the environment the structure is reshaped and adapted on a continues basis (Rogers, 2010). From a business perspective it can be perceived important to monitor these norms, structures and actors to identify windows of opportunity. Within eHealth research the focus on social actors and the social systems has often been limited to actors in the healthcare spheres. Recent literature addresses upcoming opportunities and the need for inclusion of multiple actors (Ahern, 2007; Kreps & Neuhauser, 2010). The following paragraphs are
dedicated to the theories application to eHealth from the perspectives of different academic fields. Figure 3 presents a simplified visualization of the diffusion model.
Figure 3 Visualization of the innovation diffusion process
2.3.4 An Organizational Perspective on Innovation Diffusion
The issues surrounding innovation diffusion led several academics to take an
organizational perspective and draw from both managerial-, and sociology studies to explain the processes of innovation adaption going on within healthcare institutions (Fitzgerald et al., 2002; Jennett et al., 2003; May, 2013). The combined result is the identification of organizational domains that moderate the likeliness of successful adoption of innovation within an organization. Most of the research that addressed the healthcare industry has emphasized the diffusion of telemedicine implementation. Telemedicine is defined as the use of information technology in
Knowledge Attitude Decision Implementation Confirmation Formal actors Informal actors
Communication channels Perceived: - Relative advantage - Compatibility - Complexity - Triability - Observability
delivering medical services over distance, thereby making up the clinical health domain of the broader definition of eHealth. Jennet et.al. (2003) assessed organizational readiness for telemedicine, stressing the need for technical preparation, staff preparation and openness to change. Another prominent research by Tranriverdi & Iacono (1999) presented reduction of knowledge barriers as being a major challenge for successful implementation. In an attempt to derive an applicable framework for successful diffusion of innovations in healthcare institutions Greenhalgh et.al. (2004) generated a systematic overview of these, and other studies. They propose a number of key activities and steps to improve on organizational readiness, defined by Fitzgerald et.al.(2002) as interlocking interactions. A major implication for its applicability to eHealth is that this section of research focuses on those innovations that move through networks and established health institutions. Though it has proven to be highly relevant for clinical telemedicine, it does not fit the full potential of eHealth. These can be for example, the innovations that are ought to radically change the healthcare industry and presumably move around such networks (Black et al., 2011; Koch, 2006). Hence, different approaches are needed to derive strategic insights for eHealth and preventive solutions that can be offered to anyone with computer or internet access.
2.3.5 A Psychology Perspective on Innovation Diffusion
A number of psychology studies attempted to understand the failure and rejection rate of technological innovations in healthcare institutions by looking at the acceptance of innovations by individuals (e.g. Chau & Hu, 2002; Hu et al., 1999; Wilson & Lankton, 2004). The application of different technology acceptance models confirmed the important role that perceived usefulness plays for both physicians and patients. Perceived ease of use was found to be of less importance to patients, building on the logic that the recommendation by doctors offered enough incentive for patients to accept hard to apprehend technologies (Chau & Hu, 2002; Gagnon et al., 2003; Hu et al., 1999; Wilson & Lankton, 2004). Perceived usefulness is a derivative of the relative advantage and compatibility elements as described by Rogers (2010). Similarly, perceived ease of use is derived from the element of complexity. Again however, these studies offer limited insights for eHealth innovations that are sold commercially and do not involve the need of acceptance by healthcare professionals. One can however derive the logic that perceived ease of use is likely to influence end-users in their decision to purchase eHealth when the ‘’doctor’s advice’ is absent. Furthermore, eHealth applications will in most cases be directed on consumers that are not patients per se, meaning that other barriers to acceptance may well be at play.
2.3.6 A Communication and Knowledge Perspective on Innovation Diffusion
Most applicable to the embedding of eHealth in the daily lives of everyday consumers is the section of studies involved in the communication to- and knowledge formation by consumers. These studies focus on the perceived complexity aspect as well as the knowledge and attitude formation of the Innovation Diffusion theory. A number of studies have taken this perspective, relating the lack of eHealth diffusion to barriers involved in the communication from suppliers to consumers (e.g. Anderson, 2004; Black et al., 2011; Ferguson & Frydman, 2004; Hardey, 2001; Kreps & Neuhauser, 2010). An emerging term in this field is eHealth literacy, defined by Norman & Skinner (2006) as the required skills and knowledge for proper consumer use and adoption of such technologies. Kreps & Neuhauser (2010) present this as a current issue that has two sides, namely the lacking skills by the consumer, but also the lack in skills by the suppliers to adjust and tailor their communications to the existing eHealth literacy levels. The existence of these
alignment issues are presented by the extended survey on the patterns and use of eHealth
implementations. These show that the only aspect of eHealth that has reached mass adoption is the use of internet for health related search (Anderson, 2004). Meanwhile the multitude of options ranging from online support, clinical trials, online records, to purchasing of health supplies are used by less than 10% of the population in western economies (Anderson, 2004). Simultaneously the issue is being reconciled by academics that acknowledge the communication and skill gap by depicting it as a process of transformation of consumers into e-patients (Ferguson & Frydman, 2004; Hardey, 2001). Though not designed specifically for business strategic purposes, this take on the innovation diffusion theory in the healthcare industry offers some valuable insights into the embedding of eHealth innovations.
2.3.7 Diffusion and Adoption of Preventive Innovations
eHealth inventions for behavior change have been proven successful in some cases. Examples include products that aim to increase physical activity or change dietary behaviors (Norman et al., 2007). These successful examples are however often related to areas where high degrees of societal pressures are involved. In the attempt to make our healthcare system sustainable for future generations it is important that prevention is also achieved in areas beyond those influenced by societal pressures. Recent studies have looked at this issue and developed strategic
recommendations based on a psychological analysis on preventive innovation adoption. The psychological aspects of prevention that play a major role are those surrounding controlling and changing an individual’s behavior. Research indicates the important role that eHealth can play in our daily lives towards healthier behaviors (Kreps & Neuhauser, 2010; Norman et al., 2007; Thompson et al., 2008). However, prevention often includes giving up
something (small) in the present, such as time, money or convenience, to prevent something negative from happening in the future. Marketing and psychological studies often build on the evolutionary logic, which tells that prevention goes in against the human nature based on our tendency to disregard impalpable concerns (Griskevicius, Cantú, & Vugt, 2012). This logic is applied to human engagement in sustainable behaviors, but can similarly be used for preventive medicine. Reaching the market effectively and get people to engage in preventive behaviors poses some major challenges on eHealth inventions. Research presents that acknowledging and acting upon this issue is of major importance for the effectiveness of the product (i.e. Norman et al., 2007; Thompson et al., 2008). In her most prominent work on preventive medicine, Rose (1992) concluded that the task of preventive medicine is not to tell people what they should do, but making it something they want to do. Hence it is argued that a great deal off attention in the development of eHealth behavior changing services should be focused on interaction, usability, technology presentation and other factors that may overcome issues of prevention. Overstreet et.al. (2013) found attitude, subjective norm, self-efficacy and perceived behavioral control to be the strongest predictors of an individuals intent to adopt a preventive innovation. Of these, the first two present most options for businesses to influence.
Based on the psychological reasoning across academic literature, M. Rogers (2002) has developed five strategies by which the diffusion of preventive innovations can be speeded up. These strategies are based on the aspects of relative advantage, communication channels and social interaction as defined by the theory of innovation diffusion (Rogers, 2002). The first strategy involves changing the perceived attributes of the innovation to the consumer. This is a method to increase the relative advantage. Three other strategies rely on the social structures and communication channels to promote and diffuse preventive innovations. This can be achieved by deploying champions/credible partners, inclusion of peers in the process to diffuse the product or changing the norms of the systems with the support of peers. A last stated strategy is that of including entertainment and education aspects in preventive innovations to increase willingness to use (Rogers, 2002). The application of these strategies is however context specific. For example the strategy of changing the norms on healthy lifestyles might be too challenging for any business too achieve by itself, since these norms apply to the global society. Furthermore, as the previous sections described, the healthcare industry consists of complex formal and informal structures which cannot be easily avoided or replaced. This situation has proven to affect the adoption of health related innovations in a negative way. Hence, this paper concludes that preventive eHealth solutions are in need of strategic guidance.
These strategies, together with the insights from the innovation Diffusion and Disruption theories form the foundation for a conceptual framework. This conceptual framework shall serve
as the guidance for a solid and valuable research design to uncover market deployment strategies for preventive eHealth solutions.
3. Conceptual Framework
The conceptual framework is designed as a theoretical foundation to support and guide the
research. The research itself serves to check on the value and relevance of all factors that make up the conceptual framework to conclude on specified strategic suggestions and considerations. The framework is explained along 5 propositions that are derived from the existing literature. The independent variable in this academic paper are preventive eHealth innovations that serve to support and stimulate healthy behaviors in people’s daily lives. The dependent variable is defined as market success/performance in terms of positive returns and sustainability over time.
A first step in the search for a viable business plan and commercial success is often described as the identification of market opportunities (Hwang & Christensen, 2008; Rogers, 2010). As presented in the literature review, Christensen & Bower (1995) found systematic proof that disruptive innovations address opportunities in the lower end of the market. Within the healthcare industry this would mean that entrepreneurs are to focus on lower levels of complexity of
diagnoses and treatment through for example a focus on self-care and home or work based solutions (Christensen et al., 2000). The over-served consumers in the healthcare industry are those for who disease management could move to simpler solutions (Christensen et al., 2006). Thinking a step further leads entrepreneurs towards any consumer that is not diagnosed with any disease yet, but is willing to spend on preventive technologies (Koch, 2006; Stanberry, 2000). Figure 4 presents this line of reasoning and suggests that these ‘’low end innovations’’ do not include the need for specialists and practitioners. Preventive solutions are often very well suited for self-care purposes. In addition, it is inherent to the state in which there is something to prevent that the need for close care of specialists and physicians is absent (Rogers, 2002). These
characteristics of preventive eHealth allow for business models that ‘’work around’’ the traditional healthcare structures. By this is meant that preventive innovations could be sold directly to healthy consumers or businesses and their employees for the purpose of retaining a certain health state. With this approach the slow innovating healthcare institutions and low innovation acceptance amongst healthcare practitioners can be avoided (Christensen et al., 2000; Herzlinger, 2006). The identification of these opportunities result in the following proposition:
P1: Market opportunities for preventive eHealth are mainly present outside the traditional healthcare structures in business to business and business to consumer markets.
Figure 4 The trajectories of complexity levels of diagnosis and treatment (Christensen, 2000)
Christensen et.al. (2000) stated: ‘’rather than ask complex, high-cost institutions and expensive, specialized professionals to move down-market, we need to look at the problem in a very different way.’’ Many incremental movements have been made by the healthcare industry towards highly specialized treatments for the most complex diseases (Christensen et al., 2000). The relevant literature and theories however state that these solutions, though successful, are very expensive and hence not sustainable in the long run (Herzlinger, 2006; May, 2013). It are the established incumbent firms and institutions that maintain a pace of incremental change, while being
constrained by their own systems, scale and business processes (Christensen & Bower, 1995). In the healthcare industry this suggests the need for a step away from hospital and clinic based disease management towards entirely new health solutions such as home based and preventive disease management (Koch, 2006; Kreps & Neuhauser, 2010; Stanberry, 2000). Opportunities for preventive eHealth thus lay overcoming the challenges of cost/effectiveness that are currently facing the healthcare industry (Christensen et al., 2006; Christensen & Bower, 1995). eHealth solutions do however often depend on high initial costs of R&D, marketing and installment. On the other hand, computer and internet based technologies often allow eHealth solutions to be spread easily and relatively cheap after the product-, service-, or business model design phase (Broens et al., 2007; Herzlinger, 2006). Lowering the costs of staying healthy are thus
simultaneously a challenge and an opportunity for eHealth to address. Thus far solutions have often failed to effectively present and communicate cost reductions to practitioners and
institutions. eHealth can only become widely adopted if these benefits are clearly presented. This suggestion is in line with the innovation diffusion concept of compatibility (Rogers, 2002). Achieving economic compatibility requires an understanding of how users compare the financial
benefits of previous health solutions to that of the eHealth innovation.
P2: eHealth innovations are more likely to be adopted when cost reductions opposed to previous solutions are present and clearly communicated.
The innovation diffusion theory states that communication channels play a major role in the diffusion process (Rogers, 2010). The sole focus of many eHealth innovations on channels of healthcare practitioners may partially answer the slow adoption rate since the opposite,
heterophily amongst the involved communicators, is expected to drive diffusion (Overstreet et al., 2013; Rogers, 2002). If multiple actors are active as partners in the diffusion process, the
awareness, the demand and the required foundations may thrive. Social actors, credibility partners and change agents have the power to affect decisions and attitudes towards products and services (Rogers, 2002, 2010). Thus far it has proven that innovations move slowly through the established healthcare institutions since there is simply no drive for the individual actors to change
(Herzlinger, 2006). Furthermore, it might prove especially important to influence macro level attitudes in the case of preventive eHealth solutions that will directly target B2B and B2C markets. When working around the traditional healthcare systems the question becomes which communication channels are most powerful and have most interest in making preventive eHealth succeed. The diffusion process of innovations is a social process that shapes attitudes and
behaviors (Rogers, 2002). Opinion leaders and established players are therefore most likely to influence these. Hence, it seems unlikely that eHealth can succeed in the full exclusion of healthcare practitioners and specialists. However, established players in related fields, such as electronics, insurances, and education might become important players.
P3: A multitude of communication channels, beside the traditional healthcare practitioners,
should be included in the process of diffusion for eHealth innovations to succeed.
The literature review presents the unique characteristics of preventive innovations. These mainly form a barrier that hamper the adoption of these types of products. Therefore this paper proposes that for preventive eHealth to succeed, additional attention should be paid to stimulate adoption. As is explained in the literature review, prevention is about giving up time, money or convenience in the present for an often intangible return in the future (Overstreet et al., 2013; Thompson et al., 2008). Since viable business models exclude the option of providing financial stimuli or ‘’giving back time’’ one should focus on other strategies that can make the act worth wile. Theories of innovation diffusion provide a number of solutions that can increase the willingness to act preventive. Overstreet et.al. (2013) mention the option of incorporating aspects of fun into the product. This is a form of changing the perceived attributes of the innovation that has the potential
to positively influence innovation diffusion. Furthermore, based on the notion that diffusion is a social process, multiple papers suggest the inclusion of social aspects and group interaction (Hardey, 2001; Overstreet et al., 2013; Rogers, 2002). A most basic, and often perceived to most important, is the focus on product convenience (Christensen & Bower, 1995; Rogers, 2002). Logically, convenience is a way to tackle the (perceived) inconvenience of preventive behaviors. Convenience is a broad concept that leaves many options, with the examples of creating easy access, making the action easy to perform or even include self-learning systems (Thompson et al., 2008). For these reasons this paper proposes that eHealth innovators should pay special attention to incorporating features into their products that overcome the inconveniences of acting
preventive.
P4: Stimulating the adoption of preventive innovations requires a specific focus on features of convenience, social interactionand fun.
A review of the eHealth literature has presented that adoption of new technologies is often hampered by negative attitudes towards the innovation. An analysis of the Innovation Diffusion theory puts forth three important components by which these attitudes can be positively
influenced. A first component that influences an individual’s attitude is the perceived relative advantage. This can be a number of ideas that one develops in comparison to the previous solutions and/or products (Rogers, 2010). This specific consideration might bring along some problems for the conclusions of this research since preventive eHealth solutions are in most cases radical new market disruptions (Christensen & Raynor, 2003). This has the result that for the consumer there is often no reference framework since he/she was not exposed to solutions for health prevention before. Hence, this component is essential to the model and might result in the need for specific strategic attention. A second component is that of compatibility. This covers the degree to which an innovation is perceived to be in line with needs and values that potential adopters have developed through past experiences (Rogers, 2010). Different from the previous, it is likely that people have some experiences with preventive behavior and developed certain needs and values with regard to that behavior (Norman et al., 2007). Preventive behavior is often in conflict with many people’s needs, but in line with their values (Overstreet et al., 2013). For this reason, this component is likely to influence marketing strategies. A third component is the level of complexity, also referred to as ease of use. This factor is fairly similar to the ‘’simple design’’ aspect as covered in the theory of disruption. It simply states that individuals are more likely to adopt an innovation when it easy to comprehend and to use (Rogers, 2010). Especially in the case of prevention, where benefits are often intangible, individuals might quickly reject products that require new skills or additional knowledge (Overstreet et al., 2013).
P5: Stimulating the adoption of eHealth technologies requires a specific focus on product complexity, relative advantage and compatibility.
Presented below in figure 5 is the final conceptual model in which all components, as described by the innovation literature, are incorporated. This model serves as the guiding map for the research methodology and data analysis towards the development of context specific strategic considerations.
Figure 5 The conceptual framework
Communication Channels and Sales
Networks Market
opportunities
Diffusion and Adoption of preventive innovations Preventive eHealth Innovations Market success / performance Stimulate prevention: Convenience Fun Social Interaction Technology acceptance: Complexity Relative Advantage Compatibility Mediator 1 Mediator 2 Moderator 1 Moderator 2
4. Research Methodology
The objective of this chapter is to clarify in detail the research process and the underlying reasoning for the chosen methods. The data sources, research approach and method are carefully evaluated on the aspects of validity and reliability. The chapter is structured along seven
paragraphs covering different sections of the methods, data collection and evaluations.
4.1 Research Strategy
This research is designed to uncover the process of eHealth diffusion, narrowing down on the question of how preventive eHealth solutions can successfully be commercialized. For this purpose the research aims to deliver data from sources that hold information on a variety of stakeholders that influence this process. The core of the research is of descriptive nature, looking at the who, what, when and how questions surrounding the topic of eHealth embedding. The data analysis and discussion guide the study towards explanatory answers by covering links of
correspondence and discrepancies to the relevant theories.
The analysis is executed through a narrowing-down approach, starting with macro level insights to serve as the data for PEST and stakeholder analyses, as are explained in the results section. These enable an understanding of the broader eHealth market and present which players and factors influence is structure and dynamics. This market analysis serves as the basis to narrow down to a micro/business level analysis. The latter is designed to result in conclusive strategic implications for managers engaging in the market of eHealth services and products.
The research method itself is set up to collect the highest quality of data possible within a time frame of one and a half to two months. The research process is initiated by an online-, and academic literature search to identify the richest and most valuable sources. After this search and the establishment of the literature review the most suiting research set-up is established, as elaborated in this chapter.
4.2 Scientific Approach
The decision for a specific research method is partially determined by the scientific approach. This thesis is based on a deductive approach, meaning that the reasoning works from general theory towards a specific real life situation. The reasoning starts by looking at existing theories on which expectations are based, these are then to be confirmed through observations of the real life situation. This can also been seen as a top-down approach and is in that sense is often labeled as the opposite of an inductive approach that works bottom-up, in which theory is built after pattern recognition (Bell & Bryman, 2011). However, it is to be noted the research does not look to confirm or disconfirm theory, but merely tests its applicability to the eHealth environment.
In line with the deductive approach this paper has deducted part of the codes, or
‘’themes’’ from the theory. These were thus developed prior to the analysis. The coding technique is deployed for the purpose of organizing the data, clear display of the data and it allows for a structured analysis of patterns within the findings. The results of the coding analysis are presented in different paragraphs. In these paragraphs the patterns in the line of reasoning of respondents are displayed as integrated text and occasionally supported with direct quotations to exemplify and illustrate these subjects.
In this process the collected data is analyzed along different themes that link to the theory of disruption and the literature on preventive innovations. The emergence of these themes from the interview data is called saturation and signals the completion of the data search (Dicicco-Bloom & Crabtree, 2006). The analysis will elaborate on those aspects within the identified themes that present: 1. Consent amongst the experts. 2. These areas of consent are in line with the theory. 3. These areas of consent present clear contradictions to the theory. Through this approach the theoretical framework is evaluated and possibly refuted for the subject of this paper.
4.3 Research Methods
The chosen method of data collection is a qualitative approach through the execution of semi-structured interviews. Researchers often distinguish between qualitative and quantitative research approach. Though they can be perceived being ambiguous concepts, the distinction helps to clarify the practice of business research (Bell & Bryman, 2011). Quantitative approaches use quantitative information to test relations between defined variables through statistical and
mathematical analyses. This study does not include any such objectively measurable variables and instead emphasizes the understanding, interpretation and observation of a complex subject,
referred to as the disruption or embedding of eHealth (Bell & Bryman, 2011). The latter requires a qualitative approach which allows to study phenomena in their natural environments, including entities and social actors (Denzin, 2009). In more detail, the qualitative method is most suiting since the technology of workplace health monitoring is only described in the broader eHealth context, though it requires in-depth understanding of factors influencing the success of their market deployment strategies (Black et.al., 2011; Edmondson & McManus, 2007). More
importantly, a qualitative approach allows gathering information which explains the question how preventive eHealth fits within the theory of innovation disruption (Christensen & Raynor, 2003).
The qualitative research method that is chosen to execute the research are semi-structured interviews. This is a source of primary data, collected specifically for this paper. The need for understanding rather than observing draws the research towards possibilities of in-depth interviewing, expert interviewing and/or secondary data analysis. The deductive nature of the
