The case of ehealth in the healthcare sector

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THE CASE OF EHEALTH

IN THE HEALTHCARE

SECTOR

University of Amsterdam - UvA MSc. in Business Administration Entrepreneurship and Innovation track

Supervisor: dhr. E.L. (Emiel) Eijdenberg Second reader: mw. dr. Y. (Yang) Song

Academic year: 2015-2016

Ottó Almacht UvA (11084243) Master thesis, Amsterdam, June 2016

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Statement of originality

This document is written by Student Ottó Almacht who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstr

act

This study aims to answer the research question “What is the effect of eHealth solutions (disruptive innovation) on the business performance of small- and medium-sized enterprises and on their customers?”, in addition to demonstrate the current state of eHealth and its related disciplines, available theories, and practical implications. To expand the available literature on commercial opportunities proposed by eHealth, the research examines the influence of eHealth appliances on the two major stakeholders, within the ambit of healthcare. Data was collected from two various population, 87 SME leading physicians to gauge the business performance of the enterprise, besides 146 individual who has already experienced some kind of eHealth solution, in order to measure the satisfaction level. The research resulted a significant positive effect of eHealth solutions in every aspect of the investigation. Thereby a positive association between the academic literature and the healthcare industry’s necessity for this disruptive innovation is unfolded. Implications of these findings are discussed.

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List of t

ables

and figures

Tables

Table 1. Cronbach’s Alpha SAT………32

Table 2. Cronbach’s Alpha BP………...32

Table 3. Normality SME………33

Table 4. Normality Customer……….33

Table 5. Correlation matrix SME………...33

Table 6. Correlation matrix Customer………34

Table 7. Regression analysis Hypotheses 1………34

Table 10. One-Way Anova analysis Hypotheses 2………37

Table 11. One-Way Anova analysis Hypotheses 2………37

Figures

Figure 1. Toffler (1981) Evolution of information society……….17

Figure 2. Conceptual model ………...24

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Abbreviations:

SME – small- and medium-sized enterprise GDP – gross domestic product

IS – information system

ICT – information and communication technology IoT – internet of things

IT – information technology

WE – Western Europe: (Andorra, Belgium, Germany, Netherlands, Spain, and United Kingdom)

CEE – Central and Eastern Europe: (Hungary, Romania, Serbia, Slovakia, and Slovenia)

OTH – other: (Australia, Israel, Japan, and United States)

EH1 – eHealth level 1 solutions EH2 – eHealth level 2 solutions EH3 – eHealth level 3 solutions EH4 – eHealth level 4 solutions B2C – Business to consumer B2B – Business to business C2B – Consumer to business C2C – Consumer to consumer

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1. Introduction

Across the entire globe the healthcare industry is facing persistent issues and challenges of meeting the increasing demand for services, the rising cost of those services, as well the widening gap between the ones who have and ones who do not have access to those services (Limburg et al., 2011; Christensen, Overdorf, 2000). The motivation behind this research derives from the same ascertainments in multiple healthcare systems, with the purpose of bringing to light a possible panacea, and in doing so supporting the renewal of obsolete systems. Global trends such as the IT revolution 2.0 (commonly referred to as the Internet of Things), rising power of big data, or growing importance of health in the economy; besides appearing trends such as as struggle against cyber insecurity are fostering the flourishment of new appliances to tackle these pressing problems. Information technology, electronic communication based products and services for healthcare, are commencing their global progress as they emerge and evolve. These specific innovations are acknowledged collectively as eHealth technologies.

Academics have recognized and acknowledged eHealth as a promising vehicle to overcome the increasingly urgent problems of the healthcare industry (Ahern, 2006; Ahern, 2007; Kreps, Neuhauser, 2010), and further, research has shown that the survival of healthcare is contingent on the effectiveness and efficiency of these IT related appliances’ implementation (Liaw, 2002). According to Eysenbach, an acclaimed scholar regarding this topic: “eHealth is an emerging field

of medical informatics, referring to the organization and delivery of health services and information using the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a new way of working, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using

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information and communication technology.” (Boogerd, Arts, Engelen, Belt, 2015, p.2). It is worth

noting however, that this determination cannot be pinned down, owing to the continuously mobile, dynamic environment. Although a swift increase in the number of articles related to eHealth is evident, the topic still needs to be investigated further due to the limited amount of research (Boogerd, Arts, Engelen, Belt, 2015). The available literature and healthcare professionals’ practical experience exhibits the insufficiency of strategic formation and outcome efficiency (Black et al., 2011), moreover the applicable writings on commercial opportunities proposed by eHealth solutions is still rather limited. According to researchers the future perspectives, credibility and value of eHealth lies in its capability to prove the positive effect of it (Ahern, 2006). In light of this context the following research investigates the market opportunities for eHealth, mainly outside the “traditional healthcare structure”, rather in business to customer/patient markets (primary healthcare sector), with the assistance of evidentiary performance measurements.

The central, research question of this paper is formulated as follows: “What is the effect of eHealth solutions (disruptive innovation) on the business performance of small- and medium-sized enterprises and on their customers?” The research is conducted with help of a survey strategy, because in this case its approach is the most appropriate for a cross-sectional causal relationship exploration (Saunders, Lewis, 2012), between the utilization of eHealth appliances and the major healthcare stakeholders, namely the service providing professional physicians and their patients. The paper intends to encourage the doctors to apply an entrepreneurial mindset, to be opened for these new opportunities and to pursue a mass market dissemination. While this research confirms the demand for eHealth is substantial, and the technologies are available, for world-wide application there is still a lot to do. Accurate clarification, strategic guidance, practical- and theoretical insight, and education are the necessities to overcome the barriers within the ambit of

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healthcare. This research complements this process by adding its contribution to support the renewal of healthcare systems. Structured foremost by presenting the literature review to provide the theoretical background, the subsequent section (section three) delineates the data collection procedure and research method. Results based on the collected data are analyzed in section four. Finally the most relevant conclusions and implications of the results of this research are discussed in section five, jointly with the most notable limitations and a number of suggestions for further research.

2. Literature review

In this section, literature concerning the following topics are going to be reviewed: entrepreneurship, small and medium sized enterprises, and eHealth; in addition the related academic disciplines. The literature review consists of the concepts and constructs which are essential for this research to be explained. In addition this review leads to the hypotheses which are tested with regard to answering the research question.

2.1. Entrepreneurship

Entrepreneurship could hardly be considered a new phenomenon, since the term “entrepreneur” has already been defined by an Irish economist, Richard Cantillon in the 18th_{century (Hebert, Link,}

1988). Notwithstanding the field of entrepreneurship has just started to be investigated in a systematic manner more or less recently. The topic has evolved as a business discipline by building upon, and tailoring theoretical and conceptual work from fields of: sociology, psychology, anthropology, marketing, finance, organizational behavior, management, and engineering (Kuratko, 2015). The earliest definition by Richard Cantillon describes an entrepreneur “someone

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who engages in exchange for profit; specifically, he or she is someone who exercises business judgment in the face of uncertainty” (Hebert, Link, 1988, p.21). Cantillon also specified that the most significant attribute within entrepreneurship is the risk-taking behavior, what can be for example a purchase mechanism for certain price, followed by a selling mechanism for uncertain price (Hebert, Link, 1988). Also in the 18th century Turgot and Say proposed a view, that theorizes the term “entrepreneur” as someone, whose value creating activity is the gaining and managing of production factors (Bruyat, Julien, 2000). Despite these early observers, however, the vast majority of neoclassical economists did not pay any attention to the role of the entrepreneur (Knudsen, Swedberg, 2009). Joseph Schumpeter (1934) has approached the topic differently, focused on the entrepreneur as an individual and included the need for innovation to the definition. According to Schumpeter an entrepreneur is somebody who is innovative and eager to replace the “old methods” with a new one, ergo turn a new idea or invention into a successful one (Bruyat, Julien, 2000). Nowadays it is much more widely understood within the academic field of entrepreneurship, that the attributes and ability of the person, furthermore one’s willingness to take action are assumed to determine who becomes an entrepreneur (Shane, Venkataraman, 2000). Year after year the definition of entrepreneurship has developed and altered to a large degree. Drucker revised the theory of Schumpeter, he stated that it is not fundamental that an entrepreneur establishes a new enterprise. An entrepreneur is an individual, who seeks opportunities and is ready to run a risk (Drucker, 1985). Shane and Venkataraman dispute the connection between the exploration and exploitation of lucrative opportunities and entrepreneurship (Shane, Venkataraman, 2000). Israel Kirzner had a similar approach of the topic, but he defined the alertness to profit opportunity as the essential characteristics of entrepreneurship. (Kirzner, 1977) Likewise, nowadays in the 21st century the point of view on entrepreneurship is still focused merely on entrepreneurs, moreover researchers have commenced to deliberate entrepreneurship to include social and societal aspects

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as well,by aiming to create social value, often combining for-profit and nonprofit thinking (Dacin et al., 2010). Scholars nowadays propose that entrepreneurship evolves “from an active negotiation and construction in a process where ideas and action take form in an encounter with situations of uncertainty” (Lindqvist, 2011, p.15). Eagerness around the topic of entrepreneurship expanded into a global movement from the 1980s forth (Swedberg, 2000). This so far unconcerned economic notion turned into a greatly researched theme, owing to some interconnected reasons. Examples could be the unstoppable increase of globalization, or the arousal of the small and medium-sized enterprises in Europe and the US, where the business survival depends on the consecutive innovation (Swedberg, 2000). Today, entrepreneurship serves as one of the most critical sources of economic development in most regions. According to Kuratko (2014), the effect of entrepreneurial actions are felt in every sector and level of society, particularly as it is connected to innovation, productiveness, job creation, wealth generation, and the formation of new industries.

2.2. Small and medium-sized enterprises

In the following three subsections the literature of various fields within the ambit of small and medium-sized enterprise (SME) is going to be discussed.

2.2.1. Small and medium-sized enterprises in general

According to the European Union definition an organization can be regarded as a SME, when the headcount of the staff does not exceed 250 and has an annual turnover lower than EUR 50 million and/or an annual balance sheet total not exceeding EUR 43 million (European Union, 2003). The perception about SMEs has been changed since Schumpeter theorized that large firms are more probable than SMEs, to pursue an innovation strategy (Schumpeter 1934). Günter Verheugen, a member of the European Commission, responsible for Enterprise and Industry, formulated the role

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of SME’s as follows: “Micro, small and medium-sized enterprises (SMEs) are the engine of the European economy. They are an essential source of jobs, create entrepreneurial spirit and innovation in the EU and are thus crucial for fostering competitiveness and employment” (European Commission, 2005, p.3). The statement indicates, that business operation of SMEs is considered significant and indispensable, since they contribute largely to the economic success of the developed world, in doing so they are enhancing the gross value, they are key employers, and they are possessing entrepreneurial abilities and creativity. In addition SMEs can be seen as contributor to global society, since they can be a panacea for economically underdeveloped countries, as they propone growth, in form of job opportunity and financial stability (Abor, Quartey, 2010).A research, conducted by Beck and Cull (2014) found, that a greater part of firms worldwide can be classified as SME. These enterprises deviate from larger firms in terms of available resources, such as financial capital or human resources capital (Cooper, Gimeno-Gascon, 1994). Therefore they have to think and organize their business operations differently. SMEs typically carry out unique innovation strategies due to their sensitivity to external environmental pressure (Dean, Brown et al. 1998), furthermore due to the fact, that they possess limited managerial expertise (Pissarides, 1999). Nooteboom’s (1999) research about SMEs highlighted that network ties are typical means for innovation. He stated that it is not the enterprise’s size that is substantial, but rather the level of integration and power of links (Nooteboom, 1999). This strategy explains the importance of joint ventures for SMEs, in this way they get capable to complement the deficiencies in resources they are facing (Lu & Beamish, 2006). Besides these businesses in principle are in lack of resources to manage the whole innovation process, which refers back to the importance of ties, repeating the fact, that they are more intended to collaborate with other firms (OECD, 2010). A relatively recent study by Morris and Kuratko (2010) determined, that smaller firms respond faster to new opportunities, which leads to higher

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innovativeness. More innovative firms tend to grow quicker, gain higher profit and are more productive (Geroski, Machin, 1993; Roper, Hewit-Dundas, 1998). Generally these smaller corporations have superiority in product (development or improvement) and business systems (new and/or improved business and marketing practice) innovations (Wagner & Hansen, 2005). Healthcare services can be divided into three parts: hospital care, primary care, and home care. Healthcare related SMEs are classified as a primary care group, as these services typically operate as gatekeepers for referral to secondary care implying more specialist medication. Primary health care providers are those outside hospitals. Usually they are general practitioners, dentists etc. (The European Commission, Enterprise Directorate General, 2003). Before investigating the outcome of business activities related to SMEs, it is necessary to delve into the relationship between the business activity and those persons with a ‘stake” in it.

2.2.2. SME stakeholders

Freeman’s theory claims that a stakeholder can be a group or an individual, that affects or can be affected by the output of an organization’s functions and-, objectives (Freeman, 1984). According to Donaldson and Preston, this theory has three focus areas: descriptive (outline critical relationships), instrumental (identify effective practices) and normative (critique the role and purpose of the firm) (Donaldson, Preston, 1995). Following the descriptive strategy, the identification process of key roles related to SMEs within healthcare resulted the following ascertainments. The healthcare industry brings complexity and a tangle net of obligations inherently. In an extensive research from Werhane, aiming at the stakeholder mapping of healthcare connected businesses, the following relevant stakeholders have been identified: patients /primary stakeholder/, healthcare professionals, community, investors, non-professional employees, government, lawyers and courts, and healthcare organizations (Werhane, 2000). The

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limits of this dissertation are too narrow to investigate the whole stakeholder group, accordingly the research intend to use the formal consideration, standard of fairness as the normative basis for identifying stakeholder relationships. Robert Phillips (1997) drew up this concept and it is widely accepted within the ambit of healthcare business relationships, networks and ties. The principle originates from Rawl’s theory of justice, which asserts that “Whenever persons or groups of persons voluntarily accept the benefits of a mutually beneficial scheme of co-operation requiring sacrifice or conurbation on the parts of the participants and there exists the possibility of free-riding, obligations of fairness are created among the participants in the co-operative scheme in proportion to the benefits accepted.” (Phillips, 1997, p.57) This formal consideration grant a moral minimum for stakeholder decisions, in other words different stakeholders have to meet the minimum standard of respect for other stakeholders, in terms of operations, outcomes, information transfer, etc. Summing all the above mentioned, as stated by Werhane (2000) the prioritization of stakeholders takes shape as follows. By the entity of healthcare related business, the primary stakeholders are the patients, patient population by the SME is employed. The key value creating function of these SMEs is the professional patient care, which leads us to the second most important stakeholder, the healthcare professionals (Werhane, 2000). These stakeholders are the key influencers of long-term business viability.

2.2.3. SME performance

Cameron ascribes to the view that performance is multidimensional (Cameron, 1978). Two essential aspects of performance are effectiveness and efficiency. Neely (2002) theorized that effectiveness implies to the measure to which stakeholder requisites are achieved, whilst efficiency is an extent of how economically the enterprise’s means are used in case of providing a given level of stakeholder satisfaction. A further definition derived from Moullin (2003), specifies an

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organization’s performance as how properly the enterprise is lead and the value the firm offers for customers and various stakeholders. To achieve higher performance, enterprise has to reach its expected target with superior efficiency and effectiveness compared to its competitors (Neely, 1998). Despite the fact that much research has already been carried out on the subject of performance measurement, it is still a topic of discussion. Neely (1998) theorized performance measurement as the method of quantifying the efficiency and effectiveness of past operations by procurement, coordination, classification, analysis, interpretation and dissemination of corresponding data. The enhancing pressure on healthcare related businesses force them to be first-rate in cost, quality, access, consumer choice in order to meet customer demands whilst controlling the costs (Shortell, 2000). As Moullin stated the performance of SMEs is complex, and it has to be approached from multiple sides. To cover the requirements of the measurement process the SME has to be gauged both monetary and non-monetary. Typical monetary measurement items to determine the development of performance of smaller businesses, can be for example in the following different dimensions: sales growth, revenue growth, growth in the number of employees, net profit margin, product/service innovation, process innovation, adoption of new technology, product/service quality, product/service variety (Wiklund, Shepherd, 2003). In the interest of covering the non-monetary measurement of business performance of the healthcare related SMEs, combined with Neely’s determination of business performance measurement, results that customer’s satisfaction level has to be measured. According to the priorization of stakeholders within the ambit of healthcare, the satisfaction level of the patient population by the SME is employed has to be assessed. In the following section the theory of eHealth is going to be discussed, also together the performance-influencing capabilities of world-wide web, related to doctors and patients of SMEs in the healthcare sector.

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2.3. eHealth

Prior to the topic of eHealth is canvassed, the contextual framework of it needs to be clarified. The broadest ambit around this topic is global health care.

2.3.1. Global healthcare

Across the globe healthcare services are facing the persistent issues of meeting the increasing demand for services, the rising cost of those services, and the widening gap between the ones who have and ones who do not have access to those services (Limburg et al., 2011). The increasing demand is led by a demographic driver of a life expectancy increase, from an estimated 72.7 years in 2013 to 73.7 years by 2018, enlarging the number of people over age 65 to about 580 million globally (United Nations, 2013). Healthcare related spending’s estimated increase in 2013 was around 2.8% and was valued to total $7.2 trillion, what counts for 10.6% of global gross domestic product (GDP). Between 2014 and 2018, after the global economy heals itself from a prolonged recession, the expenditures are expected to accelerate, what estimates a 5.2% percent increase, evaluated for $9.3 trillion (I. Economist Intelligence Unit, 2014). The highest healthcare spending is in the U.S., and by 2018 it is likely to reach a threshold of 17.9% of GDP (II. Economist Intelligence Unit, 2014). The recovery of the economics in Western Europe eases the pressure on the healthcare system, but it still constrains the increase of spending to an annually 2.4% over 2014-2018. The tightest budgets can be found in the countries, where the euro zone crisis had the utmost effect, such as Greece, Portugal, and Spain. The most intense recovery in health care expenses by 2018 are expected in the Northern European markets (UK, Germany, Sweden), thanks to the pursuing of one universal healthcare fund reform. In Asia and Australasia, the construction of the public health care project integrated with raising consumer wealth are perceived to uplift healthcare spending an average of 8.1% in 2014–2018. Across Latin America within the same timeframe,

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health care spending is projected to increase an average of 4.6% annually (I. Economist Intelligence Unit, 2014). According to the research of Deloitte Holding, the following major trends are anticipated to influence the stakeholders of healthcare globally:

1. Cost: is considered as the biggest issue. Countries are under pressure to keep costs low and, increase value, even in political or economic uncertainty.

2. Adapting to market forces:transformational change is taking place. Dynamic forces are coercing to rethink traditional business models, in order to better address healthcare challenges and opportunities (e.g.: increasing role of government, scale to prosper, competition for talent, improving access to care, and consumerism).

3. Transformation & digital innovation: acceptance of new digital health information technology progressions, such as electronic health records, mobile health applications, and predictive analytics is modifying the way physicians, patients, and other healthcare stakeholders cooperate.

4. Regulations & compliance: outlook of global healthcare regulatory landscape is complex and continuously evolving. The primary driver is patient health, safety, and privacy, however it can vary from country to country.Complexity enhancing factors include sudden clinical and technology changes; increased scrutiny by governments, media, and consumers; more refined risk-monitoring techniques; and coordination across organizations and regions (Deloitte, 2015).

In the following section, the sectorial background will be discussed together with the social and technical scene.

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2.3.2. Information society, ICT and IoT

Toffler (1981) conceptualized the theory of

society evolution as follows. The first wave, agrarian society already started before Christ and lasted until the 18th century, when industry started to flourish. The industrial period did not take long before the third wave penetrated and

brought communication into modern life activities and so indicated that information became crucial to everyday- and business life (Toffler, 1981). First, it is significant to ascertain the meaning of Information System (IS). O’Brien (2006) theorized IS as a system, that details elements, such as hardware, software and data resources needed to convey information in an organization. These components aggregated are led by an operation system, called Information Technologies (IT), and if communication is involved, it is designated as Information and Communication Technology (ICT). The business elaboration of ICT applications lead to e-business, consequently to eHealth (The European Commission, Enterprise Directorate General, 2004). A further technical background related trigger of booming eHealth is Internet of Things (IoT). IoT is the connection of physical things to the Internet, what enables access to distant sensor data and manage the physical world remotely. IoT is established on the vision of synergy of captured- and retrieved data, what creates an opportunity for new services that exceeds the service offered by an isolated embedded system. Smart objects (other words embedded system) are the building blocks of IoT. The novelty of this interconnected embedded system is not in the functional capability, but rather in the potential size of trillions of smart objects, that indicates solution to technical and societal issues (Kopetz, 2011). To depict the influence of these trends, the behavioral attributes of the two main stakeholders in healthcare is going to be negotiated. PwC conducted surveys among the

F ig u re 1 . (T o ff ler 1 9 8 1 )

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customers/patients of healthcare services and clinicians. The three key statements are: being more mobile, being more accessible, and being more connected (PwC, 2014, 2015)

Customers:

 60% willing to have a video visit with a physician through a mobile device  21% have used a mobile device to order a refill of a prescription

 88% willing to share personal data with their doctor to find new treatments  67% “very satisfied” with experience at a retail clinic1_{(PwC, 2014)}

Clinicians:

 81% say mobile access to medical information helps coordinate patient care  38% use email to stay connected with their chronic disease patients

 58% would rather provide a portion of care virtually

 74% say nontraditional venues (e.g., retail clinics) improve access to care (Pwc, 2014, 2015)

Various researcher groups from EU countries theorized that the improvement of healthcare requires novel thinking, new flexible solutions that involve the revolutionary advancement in ICT into the processes and structures of facilities (European Commission, 2006).

2.3.3. eHealth terminology

Field of eHealth is considered as an evolving and promising engine to tackle the restricted capacity of healthcare systems, to initiate a health behavioral change, besides to subsidize the development of chronic disease management interventions (Ahern, 2006; Ahern, 2007; Kreps, Neuhauser, 2010). The swift increase of annual article number (in 1994 under 50pcs, in 2013 above 1600pcs) entails the rapid progression of eHealth interventions (Boogerd, Arts, Engelen, Belt, 2015). The

1_{A retail clinic is a sort of walk-in medical center placed in retail stores, pharmacies that treat minor diseases}

and offer preventative services. They usually employ nurse practitioners, doctor assistant, and do not definitely employ physicians. (Scott and Company, 2006)

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first widely accepted definition of eHealth is linked to the editor of the Journal of Medical Internet Research, Gunther Eysenbach, who felt the necessity for defining eHealth: “eHealth is an emerging

field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a state-of-mind, a way of thinking, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology.”

(Eysenbach, 2001, p.1). It is worth noting however, that this determination cannot be pinned down, owing to the continuously mobile, dynamic environment. Pagliari et al (2005) and Oh et al (2005) recognized the deficiency of the term, albeit all the definitions of eHealth involved technology and health, a consistent phrase was still missing. Based on the literature review of Pagliari, from 36- and Oh from 50 different definitions, Eysenbach refined the term as follows: “eHealth is an

emerging field of medical informatics, referring to the organization and delivery of health services and information using the Internet and related technologies. In a broader sense, the term characterizes not only a technical development, but also a new way of working, an attitude, and a commitment for networked, global thinking, to improve health care locally, regionally, and worldwide by using information and communication technology.” (Boogerd, Arts, Engelen, Belt,

2015, p.2) Eysenbach (2001) in his review about eHealth declares that “e” does not stand principally for “electronic”, and lists the following ten “e” attributes, that characterize what eHealth is most of all about and what kind of opportunities can this trend entail in the future :

1. Efficiency: increasing efficiency, meanwhile decreasing costs. Doing it by avoiding unnecessary interventions, through enhanced communication and patient involvement.

2. Enhancing quality: creates opportunity to compare different providers, involving patients as added force for quality assurance, resulting streams of customers to the best providers.

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evaluation. (still an underdeveloped area).

4. Empowerment: releasing of healthcare related knowledge to patients over the internet. It unlocks new methods for patient-centered medicine and evidence-based patient choice.

5. Encouragement: true partnership between patients and professionals, decisions are made in a shared manner.

6. Education: of professionals (continuous medical training) and patients (tailored preventative information) via Internet.

7. Enabling: standardized information exchange between healthcare institutions.

8. Extending: scope of healthcare over traditional boundaries, both geographical and conceptual. Patients get able to reach services online, from simple to complex interventions.

9. Ethics: due to new forms of interaction, new threats to ethical issues can occur, such as online professional practice, informed consent, privacy and equity issues.

10. Equity: one of the biggest promises of eHealth, that underserved areas receive professional health services. However it is the biggest threat of it at the same time, since it may intensify the gap. People without money, skills, access to networks will not be able to leverage this services, unless political actions ensure equitable access for all (Eysenbach, 2001).

The above discussed eHealth related attributes leads to Hypotheses 1 and Hypotheses 2.

Hypotheses 1: The ascendant use of eHealth solutions is positively associated with the business performance of SMEs within the ambit of healthcare sector

Hypotheses 2: The ascendant use of eHealth solutions is positively associated with the customer’s

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Owing to the novelty and continuous progressiveness of this research field, there are not so many direct relevant research about the types and levels of eHealth appliances. Consequently the classification of eHealth solutions is blurred, due to the diverse approaching methods of the researchers. Hardikera and Granta (2011) conducted a literature review from 2622 items in the topic of eHealth and categorized the four main levels and exemplars of eHealth services. The identified groups of these services are the following in ascending order:

1. Health information on the Internet: own website with healthcare related information. (featured in 27 articles)

2. Custom made online health information: online appointment booking; updating electronic health records (EHR). (featured in 7 articles)

3. Online support: coaching, mailing lists and online communities, furthermore professional online training. (featured in 12 articles)

4. Telehealth: including remote consultation, monitoring and reporting (mHealth2_). (featured in 4 articles) (Hardikera, Granta, 2011).

Innovation is conducted by strategic execution and integration of information technology (IT) and information systems. Research has stated that the survival of healthcare is contingent on the effectiveness and efficiency of the implementation of these IT related appliances (Liaw, 2002). PricewaterhouseCoopers Business Service Company conducted survey on this topic. They investigated the altering of mobile health application adoption and came to the ensuing results: Within a time frame of 2013-2015 the percentage of consumers, who had at least one medical or health application on their mobile device had doubled from 16% to 32% (PwC, 2013, 2015). The company also researched the willingness of consumers to use telehealth services (e.g.: videoconference) in order to consult with a health provider instead of an in-person visit. The

2 _{mHealth is an abbreviation for mobile health, a phrase applied for the practice of medicine and public health}

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outcome highlighted that 72% of patients between age 18-44 and 43% of patients above age 45 would use telehealth services instead of face-to-face visits (PwC, 2015). The most pressing threat of eHealth booming, besides the ignorance of the needs of underdeveloped areas to join into the system, is the cybersecurity, privacy issues (Wang, Cheng, Cheng, 2006). By 2020 the estimated value of connected healthcare devices, products is going to reach $285 billion (Varelis, Williams, 2016), but it entails the threat of enhanced exposure to hackers and criminals. Hacked devices is equal to lost consumers. According to the Consumer Survey of PwC (2015) about the reaction of the customers after a hacking incident: 50% of the customers would think twice about using any connected device, 51% would think twice about using the manufacturers’ devices, and 38% would be wary of using a hospital associated with the hacked device. For these reasons a proactive and collaborative behavior is needed from both side of manufacturers and government in order to overcome the above mentioned difficulty.

2.3.4. Disruptive innovation and eHealth

The theory of disruptive innovations deals with the emergence of radical changes within business ambience that possess the ability to overcome an accepted product or service (Christensen, Bower, 1995). The conception was firstly framed by Christensen and Bower (1995) by recognizing the trend of incumbent firms failing to keep their leading positions in case of technology and market change. This approach entailed the incumbent focused perspective of theory, researching the internal causes, lacking transformative capabilities and processes of disruptive change (Christensen, Bower, 1995; Christensen, 1997). Disruptive technologies are prone to be accepted firstly be the low end markets due to their attributes, such as simplicity and low expense. Meanwhile the mass market is supplied by the sustaining products3 and services of incumbents (Christensen, Overdorf, 2000; Christensen et al., 2000). Disruptive tecnhologies usually undergo

3 _{Sustaining products are the dominant products and services of incumbent companies, developed through}

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a surviving period of finding demand, which is accountable for incumbents to miss the opportunities and to be displaced in the competition by the new entrants (Christensen, 1997). The customers usually discern these disruptive innovations as a user friendlier product or service, contrary to establish the required underlying technology is the complex task. Frequently mentioned attributes of disruptive innovations are high level or radicalness, enhanced convenience- , and lower costs compared to prior products and services (Lucas, Goh, 2009). Nevertheless further empirical studies has to be made on the topic of disruptive innovation in order to disprove what the critics made about it (Markides, 2006). Numerous studies have been asessed by a group of researchers about disruptive innovations (eHealth) related to the issues of the healthcare industry. Up to now the literature investigated two main questions: what has impeded similar innovations to come up earlier and is this disruptive innovation capable to overcome the general issues (discussed earilier: 2.3.1; 2.3.3.) of the healthcare industry (Christensen et al., 2000; Herzlinger, 2006). Christensen (2000) theorizes that the introduction of disruptive technologies in healthcare will take place through a process of disruption concentrating on the least demanding customers and developments should be made both on business model and technological product transformation. Research about the determination of eHealth solutions regarding their disruptive phenomenon resulted the following implications: Christensen and Raynor (2003) differentiated “new market disruptions” and “low end disruptions”. The latter aims at the customers, who do not require the sustaining products and services of incumbent companies. The majority of eHealth solutions pertain to this group, offering cost reduction and enhanced convenience (Anderson, 2006; Black et al., 2011). The other “new market disruption” group is more relevant to a different type of eHealth solution, which has not been offered by the current healthcare incumbents, and has been left relatively untainted in terms of market needs (Christensen, Raynor, 2003). This group covers the preventative products and services, targeting the avoidance of medicating, which entails the

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promoting of healthy lifestyle in its nature (André et al., 2009). Christensen (2000) highlights that incumbents were failing to notice the opportunity in preventative eHealth appliances, owing to their focus on cost reduction and quality improvement.

2.4. Elaboration of conceptual model

The research question of the paper corresponds with the so far discussed literature, since it sounds: “What is the effect of eHealth solutions (disruptive innovation) on the business performance of small- and medium-sized enterprises and on their customers?” The research question is disassembled in the following hypotheses:

Hypotheses 1: The ascendant use of eHealth solutions is positively associated with the business performance of SMEs within the ambit of healthcare sector.

Hypotheses 2: The ascendant use of eHealth solutions is positively associated with the customer’s satisfaction level of SMEs within the ambit of healthcare sector.

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3. Methodology

The following chapter discusses the research approach and design of this master thesis. Firstly, the research procedure is discussed, then the targeted sample and the data collection method will be presented. Thereafter the measurements and variables are discussed, which is followed by the data analysis.

3.1. Procedure /research design

The relevant philosophy of this research is positivism, as it intends to research observable and measurable variables in the principle of cause and effect. This indicates that a theory can be proposed, tested and refined until it accurately predicts reality (Saunders, Lewis, 2012). Furthermore the approach is deduction which involves the testing of a theoretical proposition by using a research strategy designed to perform this test. A key characteristics of deduction is to explain causal relationships between variables (Saunders et al., 2009), in this case between the usage of eHealth and the performance of SMEs. The survey strategy underpins this endeavor and allows generalizing the findings since it involves structured collection of data from sizeable populations. Survey enables investigation of both the healthcare related SMEs about their business performance, together with their customer’s satisfaction level. Specifically the time-horizon of quantifying data will be cross-sectional and will result in having descriptive and explanatory characteristics for the study.

3.2. Research instrument and data collection

The data was collected using a web-questionnaire which underpinned the above mentioned research goals. As the size of the SME population related to healthcare and the size of their

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customer’s population are perpetually changing, it is improbable to be able to reach a complete sampling frame. Therefore, the executed technique of sampling was a non-probability one, which combined purposive, self-selective and snowball sampling methods (Saunders, Lewis, 2012).At first hand appropriate SMEs were chosen for the research (purposive sampling) and after dispatching the web-questionnaire to the possible respondents, they were able to adjudicate whether they are willing to participate in the research or not (self-selective sampling). Additionally they were asked to forward the survey to colleagues operating corresponding services, besides to their own customers/patients (snowball sampling) (Saunders, Lewis, 2012). In the case of the customers/patients of healthcare related SMEs, the internet community and my network of acquaintances were utilized, and filtered out the appropriate ones who has already come into contact with eHealth solutions of SMEs. In this manner they could decide whether they are willing to take part in the research (self-selective sampling). They were also requested to promote the survey in order to enhance the diversity of this population (snowball sampling). This procedure ensured respondents not to be coerced to take part in the research, furthermore anonymity and confidence have been guaranteed to all of them (Saunders, Lewis, 2012).

The online-survey was constructed via the Qualtrics software which allowed separating the survey in blocks: (see surveys in Appendix)

SME survey: block 1 contained a few general questions about the operation of the business (endurance of operation, operating area, type of operation). In block 2 the respondents could find a question about the level of the utilized eHealth solution. Block 3 dealt with the business performance of the enterprise.

Customer survey: block 1 contained a few general questions (gender, age, level of education, employment-, financial status). In block 2 the respondents could find questions related to

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experienced eHealth solutions and Internet usage. Block 3 dealt with the satisfaction level of the customers.

In general, the questionnaire contained multiple-choice and matrix-table questions, which enable asking standardized questions with standardized outcomes. A formal cover letter was attached to all surveys in order to provide deeper information to respondents and ensure them of anonymity and confidence (see cover letters in appendix). Both questionnaires were translated to English, German, and Hungarian in order to increase the response rate. To guarantee the quality of translation, all surveys were reviewed by native speakers. In order to get feedback and to adjust the surveys, prior to distribution they were pre-tested. The SME questionnaire was pre-tested by acquaintance who was a leading doctor, running an SME. The customer satisfaction survey was pre-tested by master students, who had already come into contact with eHealth solution providing SMEs.

3.3. Samples

Due to the structures of my conceptual model and survey design, this research examines two populations.

1. Healthcare related SMEs

Since one part of the research aims to investigate the population of SMEs offering healthcare services, the sample was taken by looking for SME leading physicians on online interfaces, medical specialist websites, doctor lists of foreign embassies, and by leveraging my national and international acquaintance network. Therefore the national combination of this population is composed as follows: 34% Western European countries (Andorra, Belgium, Germany, Netherlands, Spain, and United Kingdom); 46% Central and Eastern Europe (Hungary, Romania,

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Serbia, Slovakia, and Slovenia); 20% Other (Australia, Israel, Japan, United States). Hungary has the biggest share from all responses with nearly 38%. The variety of the sample in terms of specialization field of doctors builds up following way: 14 different specialization, dentistry with the largest share 28% (surgery, internist, psychologist, gastroenterologists, neurologist, dentist, general practitioner, gynecologist, cardiologist, physiotherapist, ear nose and throat specialist, ophthalmologist, orthopedist, others). As regards the gender of the respondents: 64% is male and 36% is female. Altogether, the web-questionnaire was distributed to around 1350 SMEs/physicians via e-mail, of which 87 doctor completed it fully. This results a response rate of 6.44% which is much lower than expected, because it was calculated around 35% in advance according to Baruch and Holtom (2008). The survey was available for filling in nearly for two months. In addition a significant part of the responses has been received owing to the help of my national and international acquaintance network. The dropout rate of the survey is 12.6%. The low motivation or interest of doctors, high working pressure and the lack of material incentive are possible explanations of the low response rate. It should be noted, that the more loosely formulated reminder email resulted a higher response rate than the first entirely formal one.

2. Customers of healthcare related SMEs

Since the other part of the research aims to investigate the population of the customers of SMEs offering healthcare services, the sample was taken by looking for individuals who has already come into contact with eHealth appliances. It was done by utilizing my national and international acquaintance network, social media net. The total response rate is 146 from which 38% is male and 62% is female. The dropout rate of the survey is 26%. The mean age of the respondents is 30, with a lowest 16 and oldest 76. The largest share of the survey participant has a Bachelor’s degree (35%) and is employed for wages (56%). In terms of financial status, 49% of the respondents defined

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his/her financial situation as good compared to the national average, and 38% as very good. 77% of the people who filled in the survey indicated Hungary as the country, and 9% the Netherlands, where they came into contact with eHealth solution.

3.4. Variables and measures

(see items as Appendix)

As the conceptual model discussed prior depicts, three variables can be found, which affect or are affected by the others. The following part elucidates which role each of them play in the model and how the variables were measured within the survey. It is important to accentuate that all the questions are adopted from original scales of authors from peer-reviewed journals in the field of entrepreneurship and management, and healthcare.

Independent variable

Hardikera and Granta (2011) conducted a literature review from 2622 items in the topic of eHealth and categorized the four main levels and exemplars of eHealth services. The identified groups of these services were used on a four item scale in order to rank the used (in case of SMEs) or experienced (in case of patients) level of eHealth solutions:

1. Health information on the Internet: own website with healthcare related information. (featured in 27 articles)

2. Custom made online health information: online appointment booking; updating electronic health records (EHR). (featured in 7 articles)

3. Online support: coaching, mailing lists and online communities, furthermore professional online training. (featured in 12 articles)

4. Telehealth: including remote consultation, monitoring and reporting (mHealth). (featured in 4 articles) (Hardikera, Granta, 2011).

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Dependent variable

In a deep research from Werhane, aiming the stakeholder mapping of healthcare connected businesses, the following relevant stakes have been identified: patients /primary stakeholder/, healthcare professionals, community, investors, non-professional employees, government, lawyers and courts, and healthcare organizations (Werhane, 2000). The limits of the dissertation are too narrow to investigate the whole stakeholder group, accordingly I am intended to use the formal consideration, standard of fairness as the normative basis for stakeholder relationship (Phillips, 1997). By the entity of healthcare related business, the primary stakeholders are the patients, besides the key value creating function of these SME’s is the professional patient care, what leads us to the second most important stakeholder, the healthcare professionals, consequently the business that is owned by them. Above mentioned facts leads us to the two dependent variables.

Business performance of healthcare SME:

The measurement items are derived from Johan Wiklund and Dean Shepherd, who used the following ten different dimensions of performance to gauge the development of small and medium sized enterprises: sales growth, revenue growth, growth in the number of employees, net profit

margin, product/service innovation, process innovation, adoption of new technology, product/service quality, product/service variety, and customer satisfaction (α = 0.82) (Wiklund,

Shepherd, 2003). SMEs are often very reluctant to publicly reveal their actual business performance, and scholars have deliberated on the need for subjective measures (for example the Likert scale) in evaluating business performance (Zulkiffli, Perera, 2011). For this reason, the business performance of SMEs were measured with a 5 point Likert scale in a range from very bad to very good (1-very bad, 2-bad, 3-avarage, 4-good, 5-very good).

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Satisfaction level of customers:

In measuring the level of satisfaction of customers, the American Customer Satisfaction Index (ACSI) was used. The Customer Satisfaction Index is an equivalent weighted average of three questions, using the 5-point Likert scale (starting with 1- strongly disagree, 2-disagree, 3-neutral, 4-agree, and 5-strongly agree).

1. What is your overall satisfaction with your service delivery?

2. To what extent has the service met your expectations?

3. How well the service provided compare /with ideal one/ with another service without eHealth solutions? (Angelova, 2011)

3.5. Statistical procedure

Primarily the raw data of the research had to be preconditioned and cleaned in favor of keeping the level of bias as low as possible. The dataset was inspected for errors and the incomplete answers got excluded from the research. New variables were merged from certain items so as to test the hypotheses of the research. The following processes were carried out with help of IBM SPSS (ver.22) statistical software. Reliability check of measures were be carried out to ensure the consistency of them. Cronbach’s Alpha test was conducted in order to ensure that all the items in one scale measure the same, or some questions should not be used for analysis. Skewness and kurtosis of the data had to be checked in order to test normality. To test the hypotheses one-way Anova-, Regression analysis and frequency checks were carried out. The detailed analysis and presentation of results are elaborated below in the ‘Results’ section.

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4. Results

Below the results of the questionnaire study related to the research question are going to be presented and discussed.

4.1. Data cleaning

Data cleaning process was initiated by checking incomplete responses. The dropout rate of customer survey was 26%, and 12.6% of SME survey, entailing the deleting of incomplete surveys. As all of the questions were compulsory (i.e. each question needed responding in order to progress to the next question), the survey could be filtered down to include only the ones that answered the last question. Note: None of the scales used counter indicative items. All of results and tables are

reported according to guideline of UvA.

4.2. Reliability

(Cronbach’s alpha)

The satisfaction level of customers scale had admissible reliability, with Cronbach’s Alpha = .785. The corrected item-total correlations indicate that all the items have a good correlation with the total score of the scale.

The healthcare related SME business performance scale had high reliability, with Cronbach’s Alpha = .925. The corrected item-total correlations indicate that all the items have a good correlation with the total score of the scale.

Table 2. Cronbach’s Alpha BP Table 1. Cronbach’s Alpha SAT

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4.3. Normality

Kurtosis and skewness were calculated in order to check the distribution of variables. As Table 3-4 indicates, all variables are distributed normally according to the rule of thumb (between -1 and 1). Since the study investigates the effect of eHealth solutions on the dependent variables of two different populations (SME, Customer), the necessary quantity of matrixes is multiple.

Variables (SME) Skewness Kurtosis 1. Level of eHealth solution .242 -.602 2. Business performance -.075 -.288

Variables (Customer) Skewness Kurtosis 1. Level of eHealth solution .778 -.136 2. Satisfaction level -.323 .956

4.4. Correlation, Mean, Standard deviation

The mean, standard deviation and correlations of study variables are provided in following the tables. As all of the variables are measured with more items, scale means needed to be computed in the form of new variables (MEAN function). The correlation matrix (Pearson) delineates how each variable correlates with the others. Since the study investigates the effect of eHealth solutions on the dependent variables of two different populations (SME, Customers), the necessary quantity of matrixes is multiple.

Variables (SME) Mean St. Deviation 1. 2. 3. Level of eHealth solution 2.31 1.24 .925

4. Business performance 3.73 .647 .633** .925 **Correlation is significant at the level 0.01 (1-tailed)

Table 3. Normality SME

Table 4. Normality Customer

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Variables (Customer) Mean St. Deviation 1. 2. 1. Level of eHealth solution 1.82 .847 .785

2. Satisfaction level 3.47 .607 .081* .785 *Correlation is significant at the level 0.05 (1-tailed)

4.5. Hypotheses testing

Hypotheses 1: Regression, reporting of results and conclusion

Regression was performed to investigate the ability of the used level of eHealth solution to predict levels of business performance of SME.

Hypotheses 1: The ascendant use of eHealth solutions is positively associated with the business performance of SMEs within the ambit of healthcare sector.

Independent variable:level of used eHealth solution Dependent variable:business performance of SME

Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square

Change F Change df1 df2 Sig. F Change 1 .633 .401 .394 .503 .401 56.8 1 85 .000

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 14.4 1 14.4 56.8 .000

Residual 21.5 85 .254

Total 35.9 86

Table 6. Correlation matrix Customer

T ab le 7 -8 . Re g re ss io n a n aly sis H y p o th ese s 1

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As the first step of regression analysis, the necessary elements were entered: level of eHealth solution and business performance. This model was statistically significant F (1, 85) = 56.8; p < .05 and explained 40 % of variance in business performance.

Further analysis and results regarding H1, with help of survey sample to SME business performance and used eHealth solution (Note: interpretation of analyses with significant results. Tables can be found as Appendix)

 Regression test

Analyzing the effect of duration of business operation and used level of eHealth solution on the business performance of SME.

Involved variables: Duration of business operation; reference category: eHealth level 1; dummy: eHealth level 2-3-4.

R2=.488; The involved explanatory variables explain the variance of the dependent variable with 49%. This model was statistically significant F(4, 82)=19.577, p < .05

Interpretation of unstandardized B:

- Duration of business operation: B = -.01; explains that the older the SME is, thee lower is its business performance.

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- eHealth level 1-2: B =.225; explains that the SMEs, which are using level 2 solutions, are performing .225 business performance unit better, than the ones using level 1 solutions. - eHealth level 1-3: B =.376; explains that the SMEs, which are using level 3 solutions, are

performing .376 business performance unit better, that the ones using level 1 solutions. - eHealth level 1-4: B =1.008; explains that the SMEs, which are using level 4 solutions, are

performing 1.008 business performance unit better, that the ones using level 1 solutions.

All of the VIF indicators are lower than 2, which show that explanatory variables have no distorting effect on the model.

 Anova test

With the purpose of examining the relation between the locus of the SME and its business performance, “REGION” variable was created with the following subdivision: 34% Western European (WE) countries (Andorra, Belgium, Germany, Netherlands, Spain, and United Kingdom); 46% Central and Eastern Europe (CEE) (Hungary, Romania, Serbia, Slovakia, and Slovenia); and 20% Other (OTH) (Australia, Israel, Japan, and United States).

With the help of Anova hypotheses testing, a statistically significant effect was found between the region of the locus of the SME and its business performance, F(2, 84) = 7.054, p < .05. The indicator H2=14, which is radical (√14) indicates a 38% connection tightness between region and business performance.

 Symmetric measures: Cramer’s V

A statistically significant relationship was found between the region and used level of eHealth solution. The indicator Cramer’s V=.409, indicates a 41% connection tightness between region and used level eHealth solution.

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Dominant levels of used eHealth solutions by regions: WE – Level 1, Level 4; CEE – Level 1, Level 2; OTH – Level 1, Level 4.

Hypotheses 2: One-Way Anova, reporting of results and conclusion

To examine the credibility of the proposed hypothesis, the relationship between level of used eHealth solutions and customer satisfaction level was tested. A One-way ANOVA was used.

Hypotheses 2: The ascendant use of eHealth solutions is positively associated with the customer’s satisfaction level of SMEs within the ambit of healthcare sector.

Independent variable:level of used eHealth solution Dependent variable:customer satisfaction level

SS DF MS F SIG. eHealth level 3.235 3 1.078 3.048 .031 Error 50.257 142 .354 Total 53.482 145 EHEALTH LEVEL (ASCENDING ORDER) MEAN SD N 1. level 3.34 .48 62 2. level 3.58 .64 55 3. level 3.62 .63 23 4. level 3.05 .92 6 Total 3.47 .60 146

With the help of Anova hypotheses testing, a statistically significant effect was found of the level of used eHealth solutions on the level of customer’s satisfaction level, F(3, 142) = 3.048, p < .05.

T ab le 1 0 -1 1 . On e-W a y A n o v a an al y sis H y p o th ese s 2 Model Summary

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A different analysis and result of H2, with help of survey to level of customer satisfaction and used eHealth solution (Note: Tables can be found as Appendix)

 Regression test

Analyzing the effect of different levels of eHealth solution (with which the customers came into contact) on the satisfaction level of customers.

Involved variables: customer satisfaction level; constant: eHealth level 1; dummy: eHealth level 2-3-4.

R2=.06; The involved explanatory variables explain the variance of the dependent variable with 6%. This model was statistically significant F(3, 142)=3.048, p < .05

Interpretation of unstandardized B:

- eHealth level 1-2: B =.238; explains the customers that came into contact with level 2 solutions, defined a .238 satisfaction level unit higher, than the ones with level 1 solutions. - eHealth level 1-3: B =.274; explains the customers that came into contact with level 3 solutions, defined a .274 satisfaction level unit higher, than the ones with level 1 solutions. - eHealth level 1-4: B = -.294; explains the customers that came into contact with level 4 solutions, defined a .294 satisfaction level unit less, than the ones with level 1 solutions.

All of the VIF indicators are lower than 2, which show that explanatory variables have no distorting effect on the model.

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Analyzing the correlation between dimensions of two dependent variables (Note: Tables can be found as Appendix)

As an additional analysis the correlation between the perceived customer satisfaction of SMEs (tenth dimension of business performance) and actual customer satisfaction level was investigated, along the different levels of eHealth solutions. Owing to the limitations of the two different populations, the following results were established.

- Investigating the correlation within eHealth levels 1-3

Analyzing at the first three levels resulted a significant correlation (at .05 level, 2-tailed) between the perceived customer satisfaction of SMEs (tenth dimension of business performance) and actual customer satisfaction level. Pearson’s r=.997; p < .05, which indicates that the perceived- and actual satisfaction level almost completely move together within eHealth levels 1-3.

- Investigating the correlation within eHealth levels 1-4

Owing to the limitations of the customer population, correlation between the dependent variables could not be detected. Pearson’s r= -.729; p > .05, which indicates an insignificant, strong correlation in the opposite direction.

4.6. Summary of Results section

Reflecting on the hypotheses from the perspective of results section. The collected and analyzed data supports the propositions of the research, generated from the available literature and practical implications. As depicted beforehand by a conceptual model, the nascent use of eHealth technologies affects both the business performance of SME (H1) and their patients satisfaction level (H2) positively.

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5. Discussion

“What is the effect of eHealth solutions (disruptive innovation) on the business performance of small- and medium-sized enterprises and on their customers?” Below the most evident, positive result indicating findings are discussed, together with the implications within the context of the research question, with respect to the existing literature and practice. Thereafter the conclusion of this study is outlined. Finally a number of limitations of the paper and suggestions for further research is given.

In this research, the effect of eHealth solutions on the two major stakeholders of healthcare were investigated in the context of business performance and satisfaction level. Namely, it examined how eHealth solutions shape the business performance of healthcare related SMEs and their customer’s satisfaction level, furthermore what specific attributes of SMEs and customers had influence over it. Drawing upon the existing literature, the conceptual model assumed the use of increasingly sophisticated eHealth appliances has a positive effect both on the business performance of SMEs, and on the satisfaction level of patients. The first proposition of this research states that market opportunities for eHealth are mainly seen outside the “traditional healthcare structure”, in business to costumer/patient markets, namely in primary healthcare (The European Commission, Enterprise Directorate General, 2003). Although a swift increase of annual article quantity related to eHealth is evident, the topic still needs to be researched further (Boogerd, Arts, Engelen, Belt, 2015). SME related literature determines that smaller corporations have superiority in product (development or improvement) and business systems (new and/or improved business and marketing practice) innovations (Wagner & Hansen, 2005). This implies that more innovative firms tend to grow quicker, gain higher profit and are more productive (Geroski, Machin, 1993;

The case of ehealth in the healthcare sector