Controlling a successful innovation

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Controlling a successful innovation

What characterizes a diffusion-process when the goal is to discontinue or change the use of an innovation?

Caroline W. Sølberg Yakubu

Master thesis

Center for Technology, Innovation and Culture (TIK) Faculty of Social Science

UNIVERSITY OF OSLO

SPRING 2017

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Interventions to control antibiotic use in

Norway: What characterizes a diffusion-

process when the goal is to discontinue or

change the use of an innovation?

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Controlling a successful innovation. What characterizes a diffusion process when the goal is to discontinue or change the use of an innovation?

Caroline W. Sølberg Yakubu http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

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Abstract

This thesis addresses the pro-innovation bias that permeates innovation and diffusion

research. Innovation studies have devoted themselves to study how to create innovation, and not anti-diffusion. To fail to study these elements is a failure to learn about very important aspects of diffusion. Very little research has been conducted on such cases.

The purpose of this study is to contribute to the research literature with new knowledge on such problematic innovations. My main research enquiry is what characterizes a diffusion- process when the goal is to discontinue or change the use of an innovation. The area of study is antibiotics, an innovation that has been vastly successful, but its extensive use now presents an international public health issue. To study antibiotics I use a framework that combines literature on what has previously been done to control innovations gone “bad”, with diffusion theory. This framework is used to explore how interventions attempt to control antibiotic use (and thereby decrease antibiotic resistance) in Norway. By using 22 qualitative interviews, observation and documents, this study reveals that antibiotics in itself communicates

something, and thereby creates a multitude of dilemmas that are not addressed. Antibiotics are therefore not only about medicines or changed behavior. It is about professional, legal, moral, economic and ecological environment discussions that take place in the view of what is a

“correct” or “rational” (prescribing) practice. This study points out that further research into these dilemmas can help facilitate faster success in the interventions to control antibiotic use.

This study also shows that policy and (infra)structure play crucial parts in controlling antibiotic use, and that communication is often used as a means to legitimize the more controlling structural interventions. The analysis concludes with a conceptual model of how to control an innovation. As such it contributes to the research literature with new knowledge on how the theory of diffusion works in an empirical setting as it show that diffusion theory can be relevant also when it comes to anti-diffusion, but needs to be combined with other elements. It can thus be a starting-point or framework for further studies on how to control (problematic) innovations.

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Acknowledgements

Finally - My master study has now been completed at the Center for Technology, Innovation and Culture, University of Oslo. I am grateful for the opportunity to study at this forward-thinking and knowledgeable place.

In my thesis I have had the opportunity to investigate an area that is seldom focused on in innovation research. It has been demanding but also incredibly interesting to learn about antibiotic resistance, and the measures designed to control them. In this thesis I have gone in-depth into the innovation diffusion theory and focused on mechanism used to control an innovation. To do this I have been helped by several people.

First I wish to give a big thanks to my supervisor Taran Thune. Your insightful

knowledge of innovation in the health sector has been valuable, and I am grateful for your interest in my thesis, you direct, honest feedback and positive personality. Thank you so much!

Especially I wish to thank Siri Jensen and Per Magnus Mæhle. You have generous with your time and I have really enjoyed our conversations. Further I wish to thank all my informants for sharing your thoughts and time. Your interest in public health, heartfelt wish to make a difference, and dedicated work has greatly motivated me.

In the end I wish to send a huge thank you to my friends and family. You simply make me smile and enjoy the process. Writing a thesis is not so hard when surrounded by people as wonderful as you. Thank you!

July 2017

Caroline W. Sølberg Yakubu

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Abbreviations

ASP - Antibiotic Centre for Primary Care DDD - Defined Daily Dose

ECDC - European Centre for Disease Prevention and Control EEA - European Economic Agreement

ESBL - Extended Epectrum Beta-Lactamases EPJ - Electronic Patient Journal

EU - European Union

FAO - Food and Agriculture Organization FEST - Prescription and Expedition Support GP - General Practitioner

HOD - Norwegian Ministry of Health and Care Services IOE - World Organization for Animal Health

IPH - The Norwegian Institute of Public Health KTV - Colleague Based Therapy Intervention MRSA - Methicillin-resistant Staphylococcus Aureus

NORM - Norwegian surveillance of antibiotic resistance in microbes NSD - Norwegian Centre for Research Data

RAK - Correct use of antibiotics in the municipalities SKIL - Center for Quality in Doctors Offices

WHO - World Health Organization

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Figures

Figure 1: S-shaped curve……… 19

Figure 2: Adopter Categorization on the basis of innovativeness……… 20

Figure 3: A conceptual model based of diffusion ……… 26

Figure 4:A conceptual model based of “anti-diffusion”……… 27

Figure 5: Top half of an advertisement for penicillin.……….. 52

Figure 6: Survevillance atlas of Klebsiella pneumoniae in Europe 2009.………. 57

Figure 8: Survevillance atlas of Klebsiella pneumoniae in Europe 2015……… 57

Figure 10: Total sales of antimicrobial products for farm fish in Norway1981-2015… 59 Figure 11: Information campaign image 1: “Imagine if this could kill you”………….. 79

Figure 12: Information campaign image 2: “The future is in our hands. Literally.” …… 79

Figure 13: Information campaign image 3: “Terrorism. Climate change. And this.” ….. 80

Figure 14: Information campaign image 4: “The smallest (children) are the most vulnerable”..………..………..……… 80

Figure 13: Matrix over empirical findings ……….……… 93-94 Figure 14: Perceptions of the innovation……….. 95

Figure 15: Communication and social structure……….. 104

Figure 16: (Potential) users……….. 109

Figure 17: Characteristics of a diffusion process when the aim is to discontinue or change the use of an innovation………. ……..…….. 114

Figure 18: Characteristics on how interventions in Norway have attempted to control antibiotic use ……….. 116

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1 Introduction

Many articles have been written on how to successfully manage innovation, though few have dedicated themselves on how to manage an already successful innovation.

It seems the common view in innovation research is that once an innovation is successful it is put on the market, the job is then complete, and one simply has to maintain the

innovations popularity through rebranding or tweaking it to the needs of its users. There is therefore a bias in the innovation literature is the focus on what works, and not what fails.

The studies focus on how to create innovation, but not really focusing on how to stop a problematic innovation. I am not alone in claiming that there are knowledge holes in innovation and diffusion research:

Because of the pro-innovation bias we know more about (1) the diffusion of rapidly spreading innovations than about the diffusion of slowly diffusing innovations, (2) adoptions than about rejection, and (3) continued use rather than about

discontinuance. The pro-innovation bias in diffusion research is understandable from the viewpoint of financial, logistical, methodological and policy

considerations. The problem is that we know too much about innovation success and not enough about innovation failures. The later might be more valuable in an intellectual sense (Rogers 2003:111).

Rogers continues to say that diffusion research also fails “to study anti-diffusion programs designed to prevent the spread of “bad” innovations such as crack cocaine or cigarettes.

The result of the pro-innovation bias in diffusion research is a failure to learn about certain very important aspects of diffusion” (Rogers 2003:106-107).

Innovation studies are thus in a paradoxical situation. We don’t want failures or

problematic innovation, but as failures are a necessity for learning, we certainly want the lessons derived from them. So why then haven’t more people studied when innovations

“go bad”? I believe there is a knowledge gap in this area of innovation research, and think the time is right to find out more about the topic. I do this through investigating

interventions created to control one successful innovation, namely antibiotics. Antibiotics are here widely defined as a substance formed by microorganisms that kill or inhibit other microorganisms (Norsk Elektronisk Legehåndbok). Antibiotics as an innovation bears a long history, but it was just after 2^nd world war antibiotics became available for civilian use (Porter 1999). It has since become so successful and is so wide in its range that its

extensive use is now undermining its survival, and potentially the foundation stone of

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human medicine (WHO 2014). It is namely a secondary effect of antibiotics that extensive use leads to the antibiotics loosing their effectiveness: When microorganisms (such as bacteria) are exposed to antimicrobial drugs such as antibiotics they develop antimicrobial resistance. As a result the medicines become ineffective. The threat to public health is major, as expressed in the following quote from WHO:

The problem is so serious that it threatens the achievements of modern medicine. A post-antibiotic era— in which common infections and minor injuries can kill—is a very real possibility for the 21st century (WHO 2014).

Without effective antimicrobials treatment of infections and medical procedures as cancer chemotherapy, organ transplantations or surgery as caesarian sections or hip replacement become very high risk. It is presumed the major social crisis of antibiotic resistance will reach us in the next 10-20 years (WHO 2016).

Detailed scenario analysis has estimated that without policies to stop the worrying spread of antimicrobial resistance, today’s 700.000 deaths annually would become and extremely disturbing 10 million people every year. That is more people than currently dies of cancer.

As well as the tragic human cost, antimicrobial resistance bears economic costs that will continue to grow if resistance is not tackled. The cost is terms of lost global production between now and 2050 would be an enormous 100 trillion US dollars “if we do not take action”, informs the Review on Antimicrobial Resistance chaired by Jim O’Neill (2016:1).

The portrayal of antibiotic resistance as a social issue on the international agenda, and accordingly countries have been encouraged to create their own national action plans to minimize the emergence and spread of antimicrobial resistance. Correspondingly antibiotic resistance had been placed high on the Norwegian governmental health agenda, with Norwegian politicians calling antibiotic resistance the “climate crisis of the health sector”

(Høie et al. 2014), and stating “war” on antibiotic resistance (Auestad 2015). The fear for the consequences antibiotic resistance may bring is expressed in the next quote from The Norwegian Ministry of Health and Care Services:

If today’s development of increasing antibiotic resistance continues, in a short time we may be in a situation where infection risk by carrying out standard operations as cesarean, hip surgery and heart surgery will be to great. Such a development will have large consequences on the health of Norwegians. Therefore work against antibiotic resistance must be higher prioritized in the health services (Handlingsplan 2015:3).

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In this thesis I discuss what is being done in Norway to prevent further expansion of antibiotic resistance. The research questions are as following;

I. What are the main interventions to control antibiotic use in Norway?

II. What characterizes a diffusion-process when the goal is to discontinue or change the use of an innovation?

I use qualitative interviews with key informants that are involved in this work. The empirical data has described several interventions of interest, these are:

- The Action Plan made by the Norwegian government

- Colleague Based Therapy Intervention directed towards medical practitioners - Information campaign launched towards the Norwegian public

- Structural and electronic interventions

The purpose of this study is to provide information to a very relevant issue happening in the real world, right now. The thesis engages in the public health discourse by contributing with insights on technology and innovation from the social sciences. Public health is here understood as the prevention of disease and promotion of health by measures or

interventions directed at the societal level. This places the theory in a wider political context. I aim to communicate the experiences of people trying to control a successful innovation, shedding light on problems faced by those involved in interventions to control antibiotic use in Norway, and potential areas of improvement. In addition the thesis promotes a new contribution to the research literature as it can contribute and develop knowledge base, with new knowledge on how the theory of diffusion is weakened or strengthened in an empirical setting, and if it indicates areas in need of further studies.

To narrow down the scope of the thesis, I focus on interventions to control humane use of antibiotics. This is because, as we shall see, Norway uses very little antibiotics in animal husbandry. In the next part of the introduction chapter I will review examples of what has previously been done to control innovations when they have gone bad. In following part, I will discuss what can be taught from this review when it comes to studying antibiotics, as antibiotics is a unique case.

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1.1 What stops continued diffusion of “bad”

innovations?

Despite the intention of the inventor(s), sometimes innovations, new or old ones, “goes bad”. By going bad I refer to the technology having negative, undesired affects and/or hazardous consequences. There is therefore an interest in stopping the diffusion and use of such innovations. There has been only very little previous research that have examined different cases where medical drugs or other substances have ”gone wrong”, and what has been done to stop them. Because so little research has been done, there is no theory

representing this topic yet. Based on this, in the following part of the thesis I will therefore will go through examples to see what has previously been done to control such

innovations.

As we will from the next example, when medical substances have negative effects, one of the mechanisms to control it is by taking the drug off the market. One such drug was Vioxx, from a class of drugs called COX-2 inhibitors, created as a non-steriodal anti- inflammatory drug, was mainly used for acute pain such as menstrual pains or arthritis (Drugwatch 2017). In 2000, a study called Vioxx Gastrointestinal Outcomes Research, or VIGOR, showed that that Vioxx was associated with four times increased risk of heart attacks when compared to naproxen (Waxman 2005:2576). Merck, the manufacturer of Vioxx, refuted the findings saying they were unreliable and continued to sell the product for four more years (Waxman 2005:2577). It was not until 2004 Merck voluntarily

withdrew Vioxx from the market worldwide, and in 2007 the company agreed to settle all lawsuit claims, without admitting fault. In 2011, Merck agreed to pay 950 million and pleaded guilty to federal misdemeanor charge related to its shady sales tactics (Drugwatch 2017). Drugs that have similarly been taken off the market and paid settlements are Bextra and Thalidomide.

However, in other cases legal regulation or bans are control mechanism to tame the wiry technologies. For instance, lead exposure through lead paint in older houses or lead in gasoline has been described as a public health problem. Lead is a hazardous neurotoxant that has a wide range of adverse effects on human health and behavior (Reyes 2015:1581), and exposure to lead has been connected to impaired cognitive development (Wilson and Horrocks 2008), undesirable social behaviors as crime, and unwed pregnancy rates (Nevin

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2000:18). Given the concern about lead in gasoline, agencies have focused their efforts on reducing this particular source of lead. In 2002 the United Nations Partnership for Clean Fuels and Vehicles launched the goal of getting writ of leaded gasoline worldwide by the end of 2008 (Wilson & Horrocks 2008:1). Both economic and positive social effects of lead hazard control are already estimated (Gould 2009).

Corresponding to lead-exposure, asbestos, a heat resistant material often mixed with cement or glue material, first considered very useful because the fibre was flexible and durable, has later shown to increase the risk of several diseases (LaDou et al. 2010).

Breathing in even small amounts of some kinds of asbestos increase the risk of cancer, and the scientific community is in overwhelming agreement that there is no safe level of exposure to asbestos (Welch et al. 2009). In fact, evidence was found that asbestos contribute to over half of the occupational cancer-deaths in Great Britain (Rushtun et al.

2008). Therefore several countries, Norway included, have introduced bans against using asbestos in building materials and other use that increase the risk of anyone being exposed to the substance (Forskrift om asbest). Nevertheless, there are differences in when

countries have banned asbestos. In 2010, only 52 countries had introduced a ban on all forms of asbestos (LaDou et al. 2010), and other countries are yet to complete their ban on the substance.

Smoking, on the other hand, is a product that has not been made illegal, but has gone through other regulations. The many detrimental effects of smoking, for instance increased risk of death from communicable diseases have been well documented, yet numerous people worldwide continue to smoke. WHO estimated that tobacco is currently responsible for the death of about 6 million people across the world each year, with many of these deaths occuring prematurely. This includes 600.000 people estimated to die from effects of second hand smoke (WHO 2015b). In 2013, under a UN mandate to address four

noncommunicable diseases, the World Health Assembly called on governments to reduce the prevalence of current tobacco use in persons aged 15+ years by 30% by 2025 (WHO 2015b). A full ban has not been put on tobacco, though legal regulations have been made.

In 1973, Norway introduced a Law on Protection against Tobacco Injuries, a law originally restricting the sale of tobacco products to certain places, and banned advertisement of tobacco. The law included or has since been regulated to raise the age limit from 16 to 18 (§ 5), or ordered labeling of tobacco products with a warning of health hazards (§ 3). In

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1988 an addition was made on the right to protection against passive smoking, and

smoking was banned in premises or means of transportation where the public have access.

From 2004, regulations were imposed in restaurants, hotels and public entrances to be completely smoke-free (Tobakkskadeloven). Similarly, in 1989 the EU Council and Ministers for Health of Member states made a resolution where they encouraged countries to adopt laws restricting smoking in public places (EU Resolution 1989).

I have now reviewed several examples showing the handling of technologies that have had negative effects on health. However, interest in how to dismantle successful innovations that no longer are “good” is a relevant topic in several sectors. One such discussion comes from the environmental field. At the Paris climate conference in December 2015, 195 countries adopted the first-ever universal, legally binding global climate deal. The countries agreed to the long-term goal of keeping the increase in global average temperature to well below 2°Celsius above pre-industrial levels (United Nations Paris Agreement 2015:3). In accordance with the agreement, Norway has taken measures to ensure that a large part of emission cuts comes from the transport sector. Targets have been made to phase-out fuel-powered transportations, that is cars fueled by petrol or diesel, by 2025. Heavy investments are put into zero-emission vehicles (Nasjonal transportplan 2016:217). Several other countries are formulating similar programs to phase-out by 2030.

In France and Germany, the ban was expected to be far more contentious as auto- manufacturers in both countries condemned the moves to ban the internal combustion engine, saying that it would make the economy less competitive (Sorokanich 2016). Yet, in 2016, members of the German government passed a resolution to ban the sale of internal combustion engines in the European Union by 2030. Only zero-emissions vehicles would be allowed on the market after that time, according to the resolution (Sorokanich 2016).

Additionally, Norway has made measures to stimulate sale of more environmentally friendly cars or adopt new and more environmentally friendly technologies. One example is the benefits given to owners of electric cars in Norway. These benefits include: no one- time fee, no VAT on purchase, only half company tax, and an annual fee of only 455 Norwegian kroner, much less than the minimum annual fee of 2820 Norwegian kroner for gasoline cars. In addition local benefits regulated by the municipalities can consist of exemptions on toll on ferry fees, possibility of free public parking and free charging on most public charging stations (Norwegian Automobile Federation).

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These examples show shows that there is interest from several fields when it comes to deregulating technologies or innovations that have born unintended, negative

consequences. The examples also show that in the work of changing something, different fields are combined in the task: medical, judicial, social science and public policy. We have also observed that policy and bans have been important mechanisms to control technologies when they go bad. To sum um, when technologies bear unintended, negative consequences they are either taken off the market, banned, regulated by either increase the age limit for purchasing the product, or reigning in who are allowed to sell. Even pricing the product higher, putting taxes on it or simply making the product harder to get can be similar regulations. Other inducements are to make another product more affordable, or the benefits that come along with the product more attractive (such as with the electric car).

Based on the literature reviewed, technologies “gone bad” are controlled through the active approach governments do by making a political decision to do something about the

problem either through bans or regulations. This emphasizes the point of structural and political importance in creating change. Policy could therefore be a natural tool to use to control antibiotic use in Norway. But is this possible to do in the case of antibiotics?

1.2 Antibiotics as a case – a grey area?

As we saw in the previous part of the chapter, when innovations go bad they tend to be regulated through a variety of policy-interventions, and often the innovation is banned.

Yet, the innovations spoken about in the innovations gone wrong chapter are mostly

innovations that had very negative consequences – some have been highly toxic and caused serious diseases or even death. Consequently, there was a unified consensus that these products indeed were bad, and had to be dealt with. A direct ban was therefore possible in these cases. However, there is not a common consensus that all innovations with bad consequences indeed are “bad” and should be made illegal. For instance, innovations like cigarettes or alcohol are not banned, even if they have hazardous health effects. Other innovations, like antibiotics, simply cannot be made illegal. While antibiotics have serious consequences (antibiotic resistance), it is also an amazing innovation and the corner stone of human medicine. We cant put a ban on using antibiotics – we need it to treat illness.

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We can refer to the more tricky innovations as “grey-area innovations”. They are not considered completely good, but not completely bad either. We can’t get writ of them through a ban, but we cannot keep them as are without regulating them either. Therefore a more artful control has to be made on such innovations, such as age limits, changing packaging, raising prizes etc. Similarly, antibiotics are now next in line to be controlled. I am interested in investigating the ways this work is done. The case creates questions connected to what it actually means to control an innovation. Is controlling simply to stop something, or is it about something more? The case of antibiotics is therefore a unique one.

As we will see, the interventions deals with wrong use and overuse and use anti-diffusion as a strategy, but it is also about new diffusion: spreading a new practice for doctors, and a new view of antibiotics to the public.

1.3 The following structure of the thesis

The structure of the thesis is as follows: In chapter 2, I clarify the concepts I am using in this thesis. Innovation is a buzzword today, for practitioners as well as policymakers. In order to operate with a concise concept of innovation in this thesis, a definition is presented in the theoretical framework chapter. In the same chapter I present the theoretical

framework I am going to use in this thesis. The largest part of this chapter focuses on describing the theory of diffusion of innovation. Picking up on critique of diffusion theory, chapter 3, presents a research design that deals with this critique. The chapter also

discusses the methods used to gather data, considerations I have made before, during and after the data gathering, and the reliability and validity of the findings. Then follows chapter 4, presenting a general background of what antibiotics are, and what antibiotic resistance entails. I continue with a presentation of the general situation in the world and the Norwegian context. In chapter 5 the empirical findings of the thesis is presented, and each of the four interventions are presented separately. In chapter 6 the interventions are analyzed by using terms from Rogers diffusion theory. Finally, in chapter 7, I discuss what can be learned about controlling an innovation. A general model of controlling an

innovation is displayed. I conclude with the findings of the thesis and outline need for further research.

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2 Definition of terms and theoretical framework

This chapter develops the conceptual framework for this thesis. First, I define innovation and the main strands of innovation-studies are presented. Then follows a quick overview of diffusion, and the theory of diffusion that I will use in the analysis. Afterwards follows a discussion on what happens when technologies go bad. Here I speak both about the mechanism used to deal with this as well as illustrate with examples of how innovations that no longer work have been handled in the past. Finally, in the summary I present to models to explain what I have learned from the concepts of diffusion literature, and how they can be used when trying to control an innovation “gone bad”. I also discuss how I will use the concepts in my empirical study.

2.1 Definition of innovation

Often the term innovation leads to confusion, and the first thing to point out is that

invention and innovation is not the same thing. ”Invention is the first occurrence of an idea for a new product or process, while innovation is the first attempt to carry it out into practice”, claims Fagerberg (2005:4). Sometimes invention and innovation are closely linked, but in many cases there is a considerable time lag between the two, and a time lag of several decades or more is not uncommon. To be able to turn an invention into an innovation several different types of knowledge needs to be combined, as well as capabilities, skills and resources (Fagerberg 2005:5).

A broad definition claims that innovation is ”the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations (OECD Oslo Manual 2005:46). Furthermore the Oslo Manual defines innovation activities as “all scientific, technological, organizational, financial and commercial steps which are actually, or are intended to, lead to the implementation of innovations (OECD Oslo Manual 2005:47). Such a broad definition of innovation encompasses a wide range of possible innovation in the areas of product, process, marketing and organizational change. In this thesis I use Rogers’s definition of innovation as an idea, practice or object that is perceived

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as new by an individual or another unit of adoption. It matters little if the idea as

“objectively” new as measured by the time since it was first discovered. The perceived newness of the idea is determined by the individuals’ reaction to it. If the idea seems new to the individual it is an innovation (Rogers 2003:12). Before I continue to describe diffusion theory, I will not present the main strands of innovation and diffusion studies.

2.2 Innovation studies

Fagerberg defined the field of Innovation Studies in the book “Oxford Handbook of innovation”. He posts a view that innovation-studies locate its intellectual roots in the writings of Joseph Schumpeter. Fagerberg informs that for some time, there was a view that innovation was a random phenomenon, and Schumpeter was one of the first to object to this view. Schumpeter claimed that innovation was a process, and several tings affected whether an innovation would generate or not. He divided these processes into three main aspects. The first was that there was fundamental uncertainty inherent in all innovation- projects; the second was the need to move quickly so that someone else does not reap the potential economic reward of the innovation. The third aspect of innovation process was the prevalence of resistance to new ways. Schumpeter called this inertia. Inertia was at all levels of society and threatened to destroy novel initiatives, and force entrepreneurs to fight hard in order to succeed in their projects (Fagerberg 2005:9). In Schumpeter’s view, inertia was to some extent endogenous because it reflect the embedded character of existing knowledge and habit that, while it may be energy-saving, it tends to bias decision-making towards established practice, and resist new ways of doing things (Fagerberg 2005:9). This is often referred to as path-dependence or lock-in.

After Schumpeter, empirical and theoretical research on innovation has emerged. Arthur’s study emphasized that innovative firms need consider potential problems that path

dependency creates. An example of this is that if a firm select a specific innovation path early on, it might receive “first mover” advantages, but it also risks being “locked in” to this specific path through various self-reinforcing effects (Arthur 1994). Other

contributions focus on openness to new ideas and solutions, or how large firms are often dependent on external sources for innovative activity. This is due to the fundamental characteristics of innovation: most innovations consist of new combinations of existing ideas, skills, capabilities, resources etc., claims Fagerberg (2005:10). Yet, the company’s

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success would then lie in their capacity to absorb (outside) knowledge (Cohen and Levinthal 1990). Absorptive capacity is often found challenging by firms, and the “not invented here” syndrome is a well-known feature in all firms or organizations. Nelson and Winter claims that in most cases, firms develop their knowledge on how to do things. Such knowledge then consists of routines that are reproduces through practice (Nelson & Winter 1982). It has been argued that these routines may constrain the firm’s capacity for

absorbing new knowledge created elsewhere (Tushman & Anderson 1986), and can therefore be seen as a lock-in phenomenon at the firm level.

2.3 Diffusion of Innovation Research

As innovations are seen as new things or practices that have been implemented, innovation and diffusion are closely related phenomena. Diffusion has to do with the spread of an innovation. Rogers start his book “Diffusion of Innovation” by saying; ”getting a new idea adopted, even when it has obvious advantages, is difficult. Many innovations require a lengthy period of many years from the time when they become available to the time they are widely adopted. Therefore a common problem for many individuals and organizations is how to speed up the rate of diffusion of an innovation” (Rogers 2003:1, my emphasis).

Research on diffusion is an interdisciplinary field that with time has become more coherent, but draws upon fields like anthropology, sociology, education, marketing and management, education, communication, public health and medical sociology (Rogers 2003:44-45). Early studies in this have come from diffusion of agricultural innovation to farmers. In 1950, Americans Ryan and Gross provided what has become a classic study.

They researched the conditions and processes under which hybrid corn was adopted into agricultural communities in Iowa. In the study they showed the time pattern by which use of the seed spread (the so-called S-curve), investigated the functions and importance of its diffusion agencies or media, and the relationship of characteristics (personal, economic and social) of farmers, to the rapidity with which they adopted the new innovation (Ryan &

Gross 1950:663).

Similarly, in 1958, Coleman et al. in studied diffusion of the drug “gammanym”¹ in social networks of doctors. Interestingly, 60 years after the study studying diffusion of antibiotics,

1 Gammanym is often used as a code name for the antibiotic tetracycline.

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the objective is now to control antibiotic use. Colemans results suggest that there were successive stages in the diffusion of the innovation. First, social networks were operative as chains of influence as the degree of a doctor’s integration among his local colleagues were strongly and positively related to his or hers first use of the drug. These networks were operative because of professional relationships of advisors and discussion partner. In the second stage, friendship networks become operative among those doctors that are more influences in their decisions by their colleagues they meet as friends, than those whom they engage in discussions with during working hours. Finally, for the doctors who have not introduced the drug 6 months after the drugs release, the networks seem to be completely inoperative as chains of influence. The social structure seems to no longer have an effect.

Those doctors who have not responded to this influence by this time are apparently

unresponsive to it. When they finally use gammanym they presumably do so in response to influences outside the social network, such as ad journals, articles and so on, and not in response to their relations with other doctors (Coleman et al. 1957:266).

Since then, diffusion of innovations theory have continued to be investigated in public health and medical sociology, for instance in one study of efforts to promote the HPV- vaccine to youth (Rosen & Goodson 2014). Diffusion theory was also used to guide the design and implementation of a program to enhance influenza immunization at Cincinnati Children’s Hospital, USA (Britto & Pandzik 2006). Furthermore, diffusion theory has been used to investigate and/or create design to improve diffusion of family-planning methods (Valente 1996, Nyonator et al. 2005, Rogers 1971), and HIV/AIDS-prevention (Broadhead et al. 1995, Svenkerud & Singhal 1998, Bertrand 2004).

2.4 Diffusion of innovation theory

Some authors restrict the term diffusion to spontaneous, unplanned spread of new ideas.

Greenhalgh et al. claims that “the various influences that spread the innovation can be thought of as a continuum between pure diffusion (in which the spread of innovation is unplanned, informal, decentralized and largely horizontal or mediated by peers), and active dissemination (in which the spread of innovation is planned, formal, often centralized and likely to occur through more vertical hierarchies” (Greenhalgh et al. 2004:601). In this thesis I will not distinguish between active dissemination and passive diffusion, or between the two terms. I will use diffusion for both these terms.

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Ann L. Greer finds it meaningful to divide diffusion of innovation into three theoretical frameworks: Classical diffusion theory, organizational theory and political theory (Greer 1977:508). I will now focus on the classical theory represented by Everett M. Rogers, as he is considered to having written the most comprehensive guide to how to study the diffusion of innovation. According to Rogers there are four main elements in diffusion of

innovations. “Diffusion is the process in which (A) an innovation (B) is communicated through certain channels (C) over time (D) among members of a social system” (Rogers 2003:11). These four elements can be put into three clusters of influence: 1. Perceptions of the innovation. 2. Contextual factors involving communication, incentives, management and leadership. And lastly, 3. Characteristics of the people who adopt the innovation, or fail to do so (Berwick 2003:1971). They all are part of explaining the rate of the spread of the diffusion. I will now describe these main elements one by one.

2.4.1 The perceptions of the innovation

Perceptions of an innovation can predict the variance in the rate of the spread. Most powerful is the perception of benefit of the change (Berwick 2003:1971). Even if a

technological innovation usually has some degree of benefit for its potential adopters, this degree of benefit is not always clear-cut for the intended adopters. They are seldom certain that the innovation represents a superior alternative to the previous practice that it would replace. Especially when they initially learn about it. The potential advantage can impel an individual to exert more effort to learn more about the innovation. Such information seeking activated reduce uncertainty about the innovation, and a decision to adopt or reject can be made (Rogers 2003:14). The innovation decision process is therefore essentially an information seeking and information-procession activity where an individual is motivated to reduce uncertainty about the advantages and disadvantages of the innovation (Rogers 2003:14). Said more plainly, the potential adopters perform a calculation of risk and

benefit. The more knowledge they have to reduce uncertainty about the consequences of an innovation, the more likely they are to adopt it.

The perceived attributes of innovations

Some innovations such as the cellular phones or VCR required only a few years to achieve widespread adoption. Other ideas, such as the metric system, or using seat belts, required decades to reach extensive use. It is the characteristics of innovations, as perceived by

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individuals, help explain the different rates of adoption (Rogers 2003:15). Rogers use five terms for this characterization. These are: relative advantage, compability, complexity, triability and observability.

Relative advantage is the degree to which an innovation is perceived as better than the idea it supersedes. The relative advantage may be measures in economic terms, by social

prestige factors, convenience and satisfaction. Objective advantage is not important here, it is if the individual perceives the innovation as advantageous that matters. Compability is the degree to which an innovation is perceived to be consisting with existing values, past experiences and needs of potential adopters. The adoption of an incompatible innovation will often require the prior adoption of a new value system, which is a slow process. An innovation that is compatible with the norms and values of a social system will therefore be adopted more rapidly. Complexity is the degree an innovation is perceived as difficult to understand and use. New ideas that are simple are adopted more rapidly than innovations that require the adopter to develop new skills and understandings. Triability is the degree an innovation may be experimented with on a limited basis. An innovation that is triable represents less uncertainty for the individual and allows them to learn about the innovation.

Observability is the degree to which the results of an innovation are visible to others. The easier it is for individuals to see the results of an innovation, the more likely they are to adopt. Visibility stimulates discussion of a new idea as friends, colleagues, or neighbors of an adopter often requests innovation evaluation and information about it from the adopter (Rogers 2003:15-16).

Based on these characteristics, we can assume that innovations that are perceived by individuals to have a greater relative advantage, compability, triability, observability and less complexity, will be more rapidly adopted than other innovations (Rogers 2003:16).

2.4.2 Contextual factors: communication, incentives, management and leadership.

Communication channels

If diffusion is to spread, firstly, there must be some kind of communication between the potential adopters. At its most elementary form the communication process involves (1) an innovation, (2) an individual or other unit that has knowledge of or has experienced using

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the innovation, (3) another individual or other unit that does not yet have knowledge or experience with the innovation, and (4) a communication channel connecting the two units.

The communication channel is the means used to send the message from one individual to another (Rogers 2003:18).

Examples of communication channels are for instance mass media channels that transmit messages that involve a mass medium, such as radio, television, newspaper and so on. It allows one or few individuals to reach an audience of many. Yet interpersonal channels, that is a face-to-face exchange between two people, are often more effective in persuading an individual to accept a new idea, especially if the two individuals are similar in

socioeconomic status, education or in other important ways (Rogers 2003:18). Diffusion studies shows that most individuals do not evaluate an innovation based on the basis of scientific studies of its consequences, although such objective evaluations are important especially to the first people who adopt. Instead most people rely upon subjective evaluation that is conveyed from individuals that are perceived as similar and who have already adopted the innovation. Terms to describe this is homophily and heterophily.

Homophily is the degree two or more individuals who interact are similar in certain attributes, such as beliefs, education, socioeconomic status etc. Heterophily on the other hand is defined as the degree to which two or more individuals who interact are different in certain attributes. Physical and social likeness makes homophilous communication more likely to occur than heterophilous communication. Rogers claims one of the most

distinctive problems in the diffusion of innovation is that the participants are usually quite heterophilous. For instance, that the “change agent” is more technically competent than his or her clients. The difference frequently leads to ineffective communication because the two individuals do not speak the “same” language. However when the individuals are equal in their technical grasp of the innovation, diffusion cannot occur because there is no new information to exchange. Diffusion therefore demands at least some degree of

heterophily to be present between the two participants in the communication process.

Ideally the individuals are homophilous in regard to all other variables (education etc.), yet heterophilous regarding the innovation (Rogers 2003:19).

Rogers divides the innovation-decision process into five main steps: (1) knowledge, (2) persuasion, (3) decision, (4) implementation, and (5) confirmation. Knowledge is gained when an individual or other decision-making unit learns of the innovations existence and

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gains understanding of how it functions. Persuasion takes place when an individual forms a favourable or unfavourable attitude towards the innovation. Decision occurs when the individual takes part in activities that lead to a choice of either adopting or rejecting the innovation. Implementation takes place when an individual puts the innovation to use.

Reinvention is especially likely to occur at the implementation stage. Confirmation occurs when an individual seeks enforcement over an innovation-decision that has already been made, but he/ she may reverse this previous decision of there are conflicting messages about the innovation (Rogers 2003:20).

The innovation-decision process is an information-seeking and information-processing activity where the individual gradually obtains information that decreases the uncertainty about the innovation. In the first stage the individual seeks information about the cause- effect relationships involved in the innovations capacity to solve a problem. At this stage the individual wants to know what the innovation is and how and why it works. Mass media channels can effectively transmit such information. Increasingly in the persuasion stage, and especially the decision stage an individual seeks evaluation information. The individual wants to know the innovations advantages and disadvantages for his/her particular situation. Interpersonal networks with peers are particularly likely to convey such evaluating information. Mass media is not so important at this stage because it

conveys a more general message, and the individual wants specific information. That is, an answer to the question “Will the innovation be effective in my particular situation?”

Subjective evaluations by other individuals are more likely to influence an individual at this stage (Rogers 2003:21).

Humane sources of influence

Inside the systems and structures there are different people of influence. A change agent is an individual who influences clients innovation divisions in a direction deemed desirable by a change agency (Rogers 2003:27). Change agents are usually professionals with a university degree. Their professional training, and the social status means that the change agents are heterophilous from their typical clients, thus posing problems for effective communication about the innovations they are promoting.

Opinion leadership is the degree to which an individual is able to influence other individuals’ attitudes, or is able to change informal behaviour in a desired way. This

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informal leadership is not because of a formal position or status the individual has in the system. Opinion leadership is earned and maintained by the individuals’ technical competence, social accessibility and conformity to the systems norms. Through the

conformity to the norms, such as being more innovative when the social system is oriented to change, but if the systems norms are opposed to change, the opinion leaders also reflect the norm. Therefore opinion leaders serve as a model for the innovation behaviour of their followers, and they express or exemplify the systems structure (Rogers 2003:27). But be aware, - an opinion leader can loose respect if they deviate to far from the systems norms.

They can also be “worn out” by change agents who overuse them in diffusion activities.

Then the opinion leaders begin to be perceived by their peers as to much like a professional change agent and loose their credibility (Rogers 2003:27).

Becker asserts that opinion leaders are selective in their sponsorship of innovations. He divided innovations into categories of high and low adoption potential, defined as an innovations probable ease or difficulty of diffusion. These he termed HAP for high adoption potential, or LAP for low adoption potential (Becker 1970). Becker meant that different types of persons led the way in adopting the two types of innovation. Whereas HAP leaders are consistently innovative, LAP innovation leaders will only adopt one or two parts of a programme. Early HAP-leaders value and seek professional prestige. They have valuable sources of information especially through professional meetings outside their workplace and through professional journals. These early adopters become known to local community and colleagues speak to them seeking information concerning for instance costs, political risks, problems, likelihood of opposition from other groups, efficiency of the innovation and so on (Becker 1970:269). They are therefore awarded the same place as innovation opinion leaders (Greer 1977:510).

Social systems

It is as “unthinkable to study diffusion without some knowledge of the social structures in which potential adopters are located as it is to study blood circulation without adequate knowledge of the veins and arteries”, claims Katz (1961). Here, a social system is defined as a set of interrelated units that are engaged in joint problem solving to accomplish a common goal. The members or units of a social system can be individuals, organizations, informal groups or subsystems (Rogers 2003:23). Because diffusion occurs inside a social

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system, the social structure of the system affects the innovations diffusion in many ways.

Firstly, the social system constitutes a boundary within which an innovation diffuses. Both the systems social structure affects diffusion, the effect of norms on diffusion, the roles opinion leaders and change agents play, types of innovation-decisions and the

consequences of innovation. Each of these issues involves relationships between the social system and he diffusion process that occurs within it (Rogers 2003:23-24). Furthermore, structure is defined as the patterned arrangements of the units in a system. It is the structure that gives regularity and stability to human behavior in a system, and allows a certain degree of predicting behavior with accuracy. The structure therefore represents a type of information by decreasing uncertainty. For instance, a hierarchical bureaucratic

organization is such a structure. Within the formal structure there will exist interpersonal networks linking a systems member tracing who interacts with whom, and under what circumstances. Such communication structures have differentiated elements that can be recognized in the patterned communication flows in a system (Rogers 2003:24).

In the social system, innovations can be adopted or rejected. There are three main ways the social system makes the decision. Firstly, there is the optional innovation decisions are choices to adopt/reject made by an individual independent. This person decision may be influenced by the norm of the system, and by communication through interpersonal networks. Secondly, collective innovation decisions are voiced to adopt/ reject made by a consensus among the members of a system. All units in the system usually have to

conform to the decision once its made. Thirdly, authority innovation decisions are choices to adopt/ reject made by relatively few individuals in the system. The authority possesses power, status or technological expertise. Here a general individual member of the system has little influence on the authority individuals, and whatever the authority says, goes (Rogers 2003:29-30).

The social system is also important because the innovations can be adopted or rejected by either an individual member of a system or by the entire social system, which can decide to adopt an innovation by collective or authority decision (Rogers 2003:28). Three such types of innovation-decision can be distinguished. Firstly the choices to adopt or reject can be made by an individual independent of the decisions of the other members in the system, secondly collective innovation-decisions can be made by consensus among the members of a system, and thirdly authority innovation decisions to adopt or reject can be made by

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relatively few individuals in a system who possess power, status or technical expertise. A fourth category consists of a combination of two or more of these types of innovation- decisions. Also contingent innovation decisions are choices to adopt or reject only after a prior innovation-decision (Rogers 2003:29-30).

A final way social systems influence diffusion concerns consequences; desirable versus undesired consequences, direct versus indirect consequences, and anticipated versus unanticipated consequences. For instance a change agent can often anticipate and predict the innovations form and perhaps its function, but seldom the meaning, the subjective perception of the innovation by the clients (Rogers 2003:31).

2.4.3 Characteristics of the people who adopt or reject the innovation

In order to explain the rate of diffusion, one important factor is characterizing the potential adopters (Berwick 2003:1971). The rate of adoption is defined as the relative speed in which members of a social system adopt an innovation. This can often be plotted as an S- shaped curve (see Figure 1). At first few individuals adopt the innovation. They are the first movers. Soon the diffusion curve begin to climb, and as more and more individuals adopt, the more steep is the climb until eventually the trajectory of the rate of the adoption begins to level off as fewer and fewer individuals remain who have not yet adopted the innovation (Rogers 2003:12).

Figure 1: S-Shaped curve

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Gabriel Tarde first described the S-shaped curve pattern in “The laws of imitation (Tarde 1903). The curve increases slowly at first, and then rises rapidly; it then slows down and levels off. The many studies that have followed the “diffusion model” have included a categorization of the adopter, and combine this with the time of adoption (Figure 2).

Innovativeness is defined as the degree to which an individual or other unit is relatively early in adopting new ideas than other members of a system. Adopter categories, therefore, is the classification of members of a social system based upon the relative time at which an innovation is adopted. First are innovators, followed by early adopters, early majority, late majority and laggards. Innovators are different because they actively seek information about new ideas. They have a high degree of mass media exposure, have extensive interpersonal networks and are able to cope with higher levels of uncertainty about an innovation than other adopter categories (Rogers 2003:282-283). It is important not to mix innovators with opinion leaders. Innovators are risk-taking, fascinated with novelty and are socioeconomically wealthier. Locally or socially they can be viewed as a little

disconnected, actually they may even be viewed as “a little weird”. Berwick states that in health-care, physician innovators are thought of as mavericks or appear to be personally heavily invested in a specialized topic (Berwick 2003:1973).

Other fields have studied the innovator-category, and view and speak about innovators in a different way than innovation-literature. For instance, Robert Merton uses the term

localites and cosmopolites when speaking about innovators. The former term referred to

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persons whose ambition and social satisfaction derived from their participation in the local community, and the latter term to persons whose primary reward and satisfaction derived from participation in one or more functional communities such as professional groups or national communities (Merton 1949). Here, cosmopolites will be the first to get the word regarding available innovations and also be the first to adopt them. Merton therefore divides innovation-categories into ties of connection, and whether the person derives satisfaction from belonging to or participating in a group. Social ties and self-fulfillment by belonging are more important factor in Merton’s studies than in innovation studies.

The next group, called the early adopters, is quicker to adopt than average, but are not as quick as the innovators. Berwick terms the early adopter group as opinion leaders. They are locally well connected, and they are in communication with the innovators and select ideas they would like to try out. They have the resources and the tolerance of risk enough to try new things (Berwick 2003:1972). Individuals who watch the early adopters, is the next group – the early majority. The early majority group is quite local in their perspectives and mainly learns from people they know well. They rely on personal familiarity when testing a change, instead of relying on research and theory. They are more risk averse than the early adopters. Physicians in this group are more ready to try innovations that meet their immediate needs than those that are simply interesting ideas, informs Berwick (2003:1972). The next group is the late majority. The late majority looks to the early majority for signals as to what is safe. They will adopt the innovation when it appears to be the new status quo. For physicians this is the standard practice. They watch for local proof and do not find “cosmopolite” sources of knowledge trustworthy or interesting (Berwick 2003:1972). The final group is occasionally called laggards. The term laggards, misstates this groups value or wisdom. In stead they could be called traditionalists. In a study of health innovations Berwick claims laggards are “physicians who swear by the tried and true” (Berwick 2003:1972).

2.5 Criticism of diffusion theory

In the following part of the thesis I will discuss four main criticisms of diffusion research.

They are the pro-innovation bias, the individual blame bias, the recall problem and issues of inequality. The first criticism was stated by, amongst other, Greer. She criticizes the classical tradition in diffusion theory of being too positive, and claims that because most of

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the classical research occurred during a great period of faith in progress, she points out that there has been a pro-innovation bias in innovation studies (Greer 1977:509). The pro- innovation bias has to do with the assumption that all members of a social system should adopt the new innovation, that it should be diffused rapidly and that the innovation should neither be re-invented nor rejected. This bias is assumed and implied in diffusion research and leads diffusion researchers to ignore the study of ignorance of innovations, overlook reinvention or to underemphasize rejection or discontinuance of innovations. It also fails

“to study anti-diffusion programs designed to prevent the spread of “bad” innovations such as crack cocaine or cigarettes. To repeat, the result of the pro-innovation bias in diffusion research is a failure to learn about certain very important aspects of diffusion” (Rogers 2003:106-107). This view is especially evident in research funded by change agents that have an interest in the innovations successful diffusion (Rogers 2003:110).

Because of the pro-innovation bias we know more about (1) the diffusion of rapidly spreading innovations than about the diffusion of slowly diffusing innovations, (2) adoptions than about rejection, and (3) continued use rather than about

discontinuance. The pro-innovation bias in diffusion research is understandable from the viewpoint of financial, logistical, methodological and policy

considerations. The problem is that we know too much about innovation success and not enough about innovation failures. The later might be more valuable in an intellectual sense (Rogers 2003:111).

The next criticism against diffusion research is the individual-blame bias. That is the tendency to hold an individual responsible for his or her problems. Instead, one could look at the system which the individual is part of. How a social problem is defined is important in how we go about solving it and ultimately in the effectiveness of the solution, Rogers underlines (2003:119). Therefore change agents should not stereotype late adopters as traditional or uneducated and/or resistant because it could become a self-fulfilling

prophecy (Rogers 203:121). A third problem in diffusion research is the recall problem. It has to do with the time that has passed between someone adopting an innovation, and when they are asked to remember it. Diffusion research has depended on self-reported recall data from the respondents. When they remember that far back there data is not always accurate (Rogers 2003:127). Finally, the last criticism of diffusion theory I will discuss here is the issue of equality. This is a concern that diffusion researchers have not paid enough

attention to consequences of innovations. One such inattentiveness has been to the issue of how socioeconomic benefits of innovations are distributed among individuals in a social system. Unfortunately, research has shown that diffusion of innovations often widens the

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gap between higher and lower socioeconomic status segments in a system. The issue of equality has especially been noted in developing nations where an individual’s access to technological innovations is often determined by the social structure, and development agencies have tended to provide assistance especially to the more innovative, wealthier, information seeking, and more highly educated (Rogers 2003:130,133).

2.5.1 Ways of dealing with the criticism

Rogers discuss ways of overcoming these four criticisms of diffusion research. The alternative approaches to avoid pro-innovation bias are; firstly, to investigate the diffusion while the diffusion is still under way or data could be gathered at several points during the diffusion process or both before and after an intervention (Rogers 2003:112). Also

experiments can be used or simply be more careful about how they select their innovations of study. Comparative analysis could investigate a case both a successful and unsuccessful innovation from the same timeframe (Rogers 2003:113). Investigating motives and having understanding for the individuals perceptions of the innovation could also shed light on the rationality of not adapting to the innovation (Rogers 2003:115).

Overcoming the individual blame-bias can be through seeking alternatives to using

individuals as the sole unit of analysis. Researchers must also keep an open mind about the causes of a social problem and they should guard against accepting the change agents definitions of diffusion-problems, which tends to be in terms of individual-blame. Instead all the participants, including potential adopters/ rejecters should be involved in the definition of the diffusion problem. Additionally social and communication structural variables as well as individual variables should be incorporated in the research (Rogers 203:125-126). As Rogers states “defining a problem correctly and understanding individuals perceptions of the problem are the first important steps in planning an intervention” (Rogers 203:123). An alternative approach to the recall problem can be handled through gathering data in a way that reflects the time dimension more accurately.

Suggested methods are field experiments, longitudinal panel studies, use of archival record or case studies with data from multiple respondents (each of whom provides a reality check on the others data) (Rogers 2003:127). Likewise, the inequality issue can be dealt with through introducing appropriate innovation to lower-socioeconomic clients through special development interventions. Communication strategies can be used in narrowing the gap of the socioeconomic structure.

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I have, in accordance with Rogers advice, created a research design that considers the criticism of diffusion research, and incorporates the advice on how to overcome these issues. I will explain this more in-depth in the methodology chapter.

2.6 Summary

A fundamental question for innovation research has been to explain how innovations occur. Innovation research therefore, has a tradition of investigating what generates innovations. It focuses on the phases where obstacles are faced and where new things are created. Diffusion, on the other hand, has focused on the spread of an innovation, the speed and channels used to increase the rate of adoption. Unfortunately, in both research

traditions, less emphasis has been put on what happens to an innovation when it has already become successful. Once a product has reached the market, it is somehow less interesting. Neither do the traditions emphasize investigating technologies when they “go bad” (also called pro-innovation bias), nor do the traditions explore interventions where the goal is to limit or reverse diffusion.

In this thesis I explore these under-studied topics by investigating interventions to control antibiotics’ use in Norway by using diffusion theory. By using diffusion theory emphasis is put on social relations as mediating factors in effecting knowledge, attitudes and behavior.

That is because diffusion theory offers a strategy for planning communication campaigns and/or marketing plans, and seems to be a useful theory to explain how behavior is adopted by a population. The theory is here used as a theoretical basis for understanding behavior, knowledge trends, and how principles or ideas are spread, or not spread. Diffusion in communication-research is tied to the presumption that the public is effected through the work of opinion leaders.

So, what have I learned from diffusion theory? To sum up what we have seen in this chapter, Rogers defines innovation as “the process by which (A) an innovation (B) is communicated through certain channels (C) over time (D) among members of a social system”(Rogers 2003:11). According to Rogers, there were several sets of variables that effect spreading-processes in a given social system. Such variables can be perceptions of the innovation, real or perceived attributes. We can also assume that innovations that are

Controlling a successful innovation