Who said this entrepreneurship gave much emphasis on the concept of product innovation marketing and production methods?

4.1.1 Innovation, invention and diffusion

Joseph Schumpeter first began to theorise about the characteristics and dynamics of innovation in the early 20th century conceptualising innovation as a process that involved at least one of the following five outcomes (Schumpeter, 1934):

introduction of a new good;

introduction of a new method of production;

opening of a new market;

conquest of a new source of supply of raw materials or half-manufactured goods;

implementation of a new form of organization.

Schumpeter (1942) argued that innovation represents a form of ‘creative destruction’, where something new is forged from the destruction of something old. He viewed innovation as the lifeblood of capitalism:

Capitalism, then, is by nature a form or method of economic change and not only never is but never can be stationary … [It] incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one. This process of Creative Destruction is the essential fact about capitalism.

Schumpeter (1942 p.82–3).

Schumpeter also made the distinction between invention and innovation. Invention constitutes the generation and application of new ideas (e.g. new products, new methods of production), whilst innovation represents the taking advantage of inventions to generate economic benefit. He identified the entrepreneur, either an individual or collective, as the agent of innovation:

‘The function of entrepreneurs is to reform or revolutionize the pattern of production by exploiting an invention or, more generally, an untried technological possibility for producing a new commodity or producing an old one in a new way

Schumpeter (1942 p.132).

Diffusion is also distinguished from either invention or innovation. Closely associated with adoption it represents the ‘process by which an innovation is communicated through certain channels over time among the members of a social system … the planned and the spontaneous spread of new ideas.’ (Rogers, 1962 p.5–7). Importantly, we see different segments of society adopting innovations at different stages; Rogers broke these down as follows: (1) innovators, (2) early adopters, (3) early majority, (4) late majority, and (5) laggards (Rogers, 1962).

Modern definitions of innovation are heavily inspired by Schumpeter's work. For the purposes of this chapter, we adopt the definition used by Grübler et al. (2012), where innovation constitutes ‘putting ideas into practice through an (iterative) process of design, testing and improvement’ (p. 1673). Helpfully they differentiate this from invention ‘the origination of an idea as a solution to a perceived problem or need’ and diffusion ‘the widespread uptake of an innovation throughout the market of potential adopters’ (p. 1673).

Financial Actors and Innovation Directions

Joseph Schumpeter placed finance at the center of his theory of innovation, as providing the funds necessary for the entrepreneur to spring into action. However, he focused on only one type of finance: banks (Schumpeter, 1939) and did not elaborate on the question of whether different financial actors' characteristics might impact what innovation is being financed, thus, creating directions. The Miller-Modigliani theorem, which states that sources of finance (equity or debt financing from any actor) do not matter to firms and hence do not affect the real economy (Modigliani & Miller, 1959) has further detracted attention away from distinguishing between types of finance in innovation. In subsequent literature, the only types of actors typically singled out were “government” and “venture capitalists” (Hall, 2002). The job of the former was to overcome underinvestment in research due to the positive externality of knowledge (Arrow, 1962); the purpose of the latter was to overcome information asymmetries that led to underinvestment into product development by new firms or “ventures” (Hall & Lerner, 2009). In this literature, finance takes a passive role regarding what is being financed. More recent work has placed greater emphasis on different types of financial actors and how they may impact the characteristics of the firms and technologies they are financing. Thus, financing by the public sector also beyond the R&D stage (Mazzucato, 2013) in areas such as space, health, and low-carbon technology has resulted in the creation of whole new sectors, often through mission-oriented projects that were actively decided upon by those who provided the finance (Foray, Mowery, & Nelson, 2012). In the private sector, certain actors were also pushing particular sectors or technologies (Mazzucato & Wray, 2015). But what gets financed may equally be influenced by what is neglected by certain actors: it has been noted that venture capital has often avoided very early-seed investments and has also been biased toward particular areas such as IT and biotech, only recently getting interested in green-tech (Lerner, 2012). Some studies have examined how short-term speculative financial actors have affected science-based industries (Lazonick & Tulum, 2011; Pisano, 2006).

Conversely, the literature concerned with directions has paid little attention to the role of finance in setting these directions. The directionality literature (Stirling, 2010a, 2011) in innovation studies has stressed the importance of recognizing the multiple pathways and directions that innovation can take, so that policies explicitly recognize the forces influencing them, including the risk of suboptimal policies and lock-ins. This strand of literature has focused on the role of power relations, such as those embodied in public financing of innovation. However, it has ignored how the distribution and characteristics of private and public financial actors can affect the direction of change. Similarly, economic studies considering path dependence in innovation (David, 1985) and the role of feedback effects in creating “lock-in” (Arthur, 1989) have not included the way that financial institutions can affect this dynamic.

6.2.4 Endogenous renewal cycle IS

Depending on the design of Schumpeter, they are the recurring fluctuations that can make the dynamic movements appear with certain regularities which give them their cyclical characterization (Courvisanos, 2012; Katz, 2006). Thus the dynamics of IS has a form of a nonlinear cycle that can facilitate the transfer of knowledge and learning during the generating hidden process of innovation. In addition, the continuous generation of knowledge can be considered as the accelerating endogenous driver of collaborations between all different actors of system. This is through continuous and successive innovations. For this reason, innovation does not only become a production process in which knowledge is created endogenously, but also the endogenous innovation assumes growth of economies (Goodwin, 1990a). The role of innovation funding and investment policy for sustainable development is studied by Baumol and Wolff (1992). Following that, only the knowledge production and investment can explain that fundamental systematic transition.

So, we can speak about endogenous factors that can explain the endogenously generated deterministic cycles, such as conditions of the emergence of endogenous fluctuations. According to this game of persistent innovation dynamics as conditioned by the endogenous dynamism of knowledge production mechanism, we can prove the cyclical mechanisms of persistent innovation dynamics as engine to the endogenous renewal cycles. In order to respect the continuous propriety of game of innovation, these innovative cycles are seen as simple harmonic oscillators. They are produced due to the endogenous deterministic dynamics generated by the dynamic system itself. Indeed, the evolution of this process cannot be resulted from simple exogenous quantitative change of its factors of production, but rather it comes from a qualitative endogenous instability of process that self-maintains change in the vicinity of the dynamic equilibrium of the system when it loses its endogenous stability (Goodwin, 1990a). By design, mechanism is realized only through the dominance of endogenous pulse that can include a factor of evolution as innovation.

Though, to keep the continuous dynamics innovation, we can provide a creative IS that becomes vital and suitable for the emergence of endogenous structures in the sense that it is able to define a reproduction process of invariance in which knowledge is created. The perpetual innovation cycles can explain endogenous cycles of innovation as endogenously generated deterministic cycles (Mella and Colombo, 2014). So, without continuous and relentless innovation, it would be hard for any innovation to be considered as a path-dependent outcome. For that, if we admit that the continuity property of successive innovations over time, we can expect that they are created endogenously via innovation cycle by facilitating knowledge transfer and learning.

However, this is the origin of dynamic endogenous cyclic structures (McCullough, Huffaker, & Marsh, 2012). It includes products of creations of the practical implementation of new manufacturing process. Furthermore, analysis of Mella and Colombo (2014) and Hirooka (2003) shows not only that innovation is dynamic and endogenous phenomenon but it also has a complexity feature. In addition, Kuhlmann and Rip (2018) states that there is a relationship of interaction and complementarities between innovation and the complexity induced by the instability of the system. Recently, McCullough et al. (2012) has assumed not only that the instability is an endogenous weakening the mechanism, but also the renewable energy expansion can be induced by endogenous factors, mainly the impulse of endogenous cycles.

This confirms the foundation of the endogenous cycle theory where recurrence dynamic will be capable of generating self-maintaining endogenous cyclical fluctuations that are intrinsically linked to the operation of the system itself, without being excited by another source of exogenous (Hirooka, 2003). In this sense, on the basis of “innovation clusters” and the principle of “creative destruction” (Courvisanos, 2012) postulated that innovation dynamics is a cyclical phenomenon characterizing intrinsic dynamism of the renewable energy system itself. It is at the origin of determining endogenous cycles.

In this sense, by adopting a clear and continuous process of recreating the knowledge, capable of providing the sustaining of a different level of knowledge dynamism, and diversifying the fields of innovation, Floricel and Dougherty (2007) give the fundamental dynamic feature for which these endogenous cycles must be regenerated whereby they will be persistent and renewed to ensure their dynamical continuity over time. Therefore we speak of the new concept of endogenous renewal cycles. The problem at this analytical level is to know how to maintain an effective control at the level of persistency of the endogenous renewal cycles of innovation dynamic system. But, in our mind, it will be slightly more interesting to provide an answer to this problematic especially in the presence of complicated dynamical behavior as a chaotic dynamics.

All these studies are limited and lacked some detailed empirical developments. Their limitations come first by the lack of a subjective assessment and diagnosis of the real symptoms of the involved phenomenon complexity. Second, they are unable to explain the morphology of endogenous renewal innovation cycle or to clarify its role in sustainable development. So, the most interesting in our study is to focus attention to explain the endogenously genesis of the continuous innovation cycle in the case of continuous time. But, first of all, we begin with a general and precise description of the structure of the renewable energy IS in Tunisia.

Endogenous Technology Learning and Diffusion

Modern economic analysis of technological change originates largely with the work of Joseph Schumpeter. He stressed the existence of three necessary conditions for the successful deployment of a new technology: invention, innovation, and diffusion. Invention involves the development of a new technical idea, and innovation refers to the process in which the technology is commercialized through cost reductions and thus brought to market (e.g., the learning process). Finally, diffusion is the gradual adoption of the new technology by firms, who then also decide on how intensively to use the technology. The main thesis of learning models is that cost reductions will be achieved gradually as a result of learning-by-doing activities. A windmill is not built because it is cheap and efficient, but rather it becomes cheap because it is built and operated. Still, one principal reason why wind generators invest in new capacity is because previous investment and R&D activities have brought down the costs of generating wind electricity. This suggests that innovation (learning) and diffusion are endogenous, that is, they are simultaneously determined (or at least this should be tested for).

Technically endogeneity implies that in the learning eqn [8], the regressor ln Zt and the disturbance term εt are negatively correlated. This means that estimation by ordinary least squares (OLS) would yield biased and inconsistent estimates of δL and thus of the learning-by-doing rate. In order to choose between a model specification that permits simultaneity and one that does not, one can employ the so-called Hausman specification test. If this test suggests that we should not reject the null hypothesis that ln Zt is an exogenous variable in the learning equation, we can use instrumental variable techniques to correct for endogeneity. Specifically, we regress ln Zt on a set of variables considered exogenous to ln Zt and then employ the fitted values from this first regression as instruments in place of ln Zt in eqn [8].

The issue of endogeneity is addressed in a number of previous wind power learning studies, and the empirical experiences show that the estimated learning rates can be significantly influenced by acknowledging this issue. There is empirical support for rejecting the null hypothesis of exogenous cumulative capacity in learning models. Still, there should be room for more sophisticated analyses as well. For instance, sufficient econometric decomposition of panel data could address some of the causal relationships between cost reduction and greater market penetration of the technology.

The interaction between technology learning and diffusion is important also for analyzing the role of public support policies in the wind power sector. The role of price subsidies – and, in particular, the so-called fixed feed-in price system – has been significant for the development of wind power during the last decades, but previous research suggests that there may be a need to carefully design the time development of the tariff levels. Increases in the feed-in price for wind power promote diffusion of wind power capacity, which in turn encourages the learning activities that generate cost reductions. However, there exists also a direct negative effect of feed-in price increases on cost reductions. The reasons for this are that high feed-in prices: (a) induce wind power producers to select high-cost sites (e.g., locations with expensive grid connections and/or poor wind conditions) and (b) tend to discourage the competitive pressure from other energy sources, and – as a result – innovation activities become less attractive.

This notion has important policy implications since it suggests that there exists an opportunity cost in the promotion of new technologies. Diffusion encourages learning but the measures implemented to bring about diffusion may in themselves deter innovation activities. For this reason, clearly announced gradual decreases in feed-in tariff levels over the lifetime of the windmill could be an important element of an efficient renewable energy technology policy. Recent policy developments also move in this direction, for example, the German so-called Renewable Energy Sources Act of 2000 stipulates decreasing feed-in tariffs for wind power over the years in order to take into account technical progress over the lifetimes of the mills.

Studies Including Financial Development

Long before examining the effects of financial development to the emission levels globally, Schumpeter (1911) set the foundations for the relationship between financial development and economic growth: a well-developed financial system that mobilizes savings and provides access to capital for investment, enhancing domestic production by allocating funds accordingly. Shahbaz (2009) also discusses the importance of the sector in reducing transaction, information, and monitoring costs. Shahbaz (2013) adds that the “financial sector also encourages investment activities by issuing loans at a cheaper cost and allocates resources to productive ventures, mobilizes savings, enabling trading, offering hedging, diversifying the risks, monitoring the working of the firms, and directs the firm to use environment friendly technology to enhance the level of domestic output.”

Halkos and Sepetis (2007), He and Wang (2012), and Halkos and Polemis (2016) acknowledge the lack of consensus in the literature that deals with the relationship between environmental pollution and financial development. According to Frankel and Romer (1999), the process of financial development promotes adoption of cleaner technologies and thus reduce the environmental effects of higher growth; however, studies by Zhang (2011) and Shahbaz and Lean (2012) conclude that through stimulation of economic activity, efficiency in the stock market, and attraction of Foreign Direct Investment (FDI) lead to increases in emissions. Bello and Adimbola (2010) concluded that financial development, measured by stock market capitalization, contributed to the emissions of Nigeria. This intensification of emissions was attributed to the lack of loan monitoring to investment projects.

Developing an accessible and sophisticated financial sector can promote the adoption of environmental-friendly technologies (Birdsall & Wheeler, 1993; Frankel & Rose, 2002) and thus, contribute in the reduction of greenhouse gas emissions. In addition, a developed financial sector has the potential to lower emissions by higher efficiency in the energy sector as well as promotion of technologic innovations (Tamazian & Rao, 2010; Tamazian, Pineiro, & Vadlamannati, 2009). Low-borrowing costs also assist national and local governments to embark on environmental projects (Shahbaz, 2013). For example, Tamazian et al. (2009) studied the relationship between economic and financial development and CO2 emissions for the United States, Japan, and the BRIC countries confirming that both economic and financial growth has an impact in reducing CO2 emissions.

Finally, studies have also at times shown a lack of relationship between financial development and emission levels. Ozturk and Acaravci (2013) found that financial development had no impact to CO2 emissions levels in Turkey; whereas Lee, Chen, and Cho (2015) reject the EKC hypothesis for a panel of 25 OECD countries for the period of 1971 to 2007 with the inclusion of financial development.

3.3 The myth that money is a neutral veil

Most economic textbooks proclaim that money is essentially a neutral medium of exchange, a kind of “lubricant” (Ingham, 2006) to enable the efficient exchange of goods and services of differing values (e.g., Jones, 1976; Clower, 1984; and critique in Ingham, 1996a). Economic textbooks usually claim this was as humanity's ancient solution to the problem of barter (see critique of this popular view in Graeber, 2011). A barter economy is very difficult to maintain if different people have different levels of need for many different types of goods and services. Orthodox textbook economists suggest ancient societies therefore invented money as a medium to reflect the values of goods and services, so that these could be easily, fairly and smoothly exchanged.

Money in this view is not part of the “real” economy but, as Schumpeter (1954) observed in his critical account of this view:

Money enters the picture only in the modest role of a technical device that has been adopted in order to facilitate transactions . . . so long as it functions normally, it does not affect the economic process, which behaves in the same way as it would in a barter economy: this is essentially what the concept of Neutral Money implies. Thus, money has been called a “garb” or “veil” of the things that really matter … (Schumpeter, 1954, p. 277).

Ingham (1998, 2011) notes that one of the reasons this view tends to prevail in textbook economics can be traced to a major split between economics and sociology in the first half of the 20th century. Sociology and economics were not always distinct disciplines which theorized the social and the economic in separate spheres. Early social theorists such as Karl Marx (1818–1883), Max Weber (1864–1920) and Emile Durkheim (1858–1917) made little distinction between economics and sociology (Morrison, 1995). But a rupture in sociologists' theorizing of money came with the so-called “Methodenstreit” (dispute over method) between economists and sociologists early in the 20th century. As Ingham explains:

As a result, money fell under the jurisdiction of economics, and this fact alone explains sociology's indifference; but it was the particular ‘theory’ held by the victorious economists that was to have a significant impact on both disciplines' understanding of money. After the Methodenstreit, economic thought became dominated by the idea that money was epiphenomenal—that is to say, it was treated as a neutral ‘veil’ over the underlying ‘real’ natural economy. (Ingham, 1998, p. 4)

Hence, for example, although sociologist Talcott Parsons deliberated about money and its effects within society, he assigned the question of what money is, to economists (Parsons, 1937). Giddens (e.g., Giddens, 1979; Giddens, 1984; Giddens, 1990) discussed the role of trust in enabling the money system to operate, and offered lengthy commentary on Marx's theory of money in capitalist society as a source of workers' alienation from the rewards of their labor, but did not explore the ontology of money either in general or in terms of his own enormously influential and insightful structuration theory. Pierre Bourdieu (1979, 1983, 1996) explored how monetary and social capital reinforce people's existing socioeconomic status, but did not turn his tools of analysis to the question of what money is or how money gets there in the first place. These theorists' assumptions as to what money is are those of the mainstream economics of the time. Mizruchi and Sterns' (1994) comprehensive review of the place of money in sociology of the latter half of the 20th century showed that sociology simply took the existence of money for granted.

Three quite decisive critiques can be brought against the view that money is a neutral veil. Firstly, there is no historical evidence that money arose from barter or from any other human activity, as noted above.

Secondly, the barter/neutral veil view presupposes what it purports to argue. As Orléan (1992) points out, it is based on the assumption that there is already a numerically graded market, logically prior (even if not historically prior) to the advent of money. Grading the values of different types of goods and services against each other already presupposes there is some neutral scale against which to grade them. You first have to have something that serves as money, before the grading can begin. Hence Aglietta and Orléan (2002) argue that a stable market of mixed goods and services is not possible without something that already serves as money.

But the strongest criticism of this view is that it ignores the empirical evidence of how we see money being created, whether in modern central and private banks, medieval promissory notes, complementary currencies, or informal IOU's. All these involve the creation of credit–debt relationships, obligation and entitlement, which are all basic human activities. Money is a very human phenomenon, not a neutral entity that stands outside humanity and interfaces with people across a human/non-human abyss.

Ingham (2011) and Lazzarato (2015) argue that it suits neoliberalism to foster the neutral veil view of money. If money is a neutral veil that simply reflects the real values of goods and services, then governments should not intervene in markets by taxing high incomes at higher rates than low incomes, by redistributing money to low income households, or by running public services like health, education and transport. They should privatize all services and leave it to the markets to set the most efficient prices for these. This includes energy markets, and it precludes universal benefits designed to lift people out of poverty and therefore out of fuel poverty.

Soederberg (2014) adds that fostering the view that money is a neutral mechanism of exchange between free agents in a fair market draws attention away from the reality that money, based as it is on relationships of obligation and entitlement, “is an all-power disciplinary apparatus” that involves “raw social violence, suppression, exploitation, inequality struggles and class power relations …” (quoted in Antoniades, 2018)—particularly in today's setting of extreme inequality.

Bioenergy entrepreneurism

Entrepreneurism is important because entrepreneurs drive innovation and technological change and thus generate economic growth (Schumpeter, 1934). Bioenergy entrepreneurs drive growth of the bioenergy sector. This chapter discusses bioenergy entrepreneurism, including market drivers, entrepreneurial motivations, and opportunities for bioenergy entrepreneurs. Market drivers such as policy, energy use, and the environment are moving the energy sector toward more renewables. Entrepreneurs are motivated by economics, the environment, social factors, and a pioneering spirit. By exploring the market drivers and entrepreneurial motivations, one can more clearly visualize and seek out opportunities to participate in the bioenergy sector. Bioenergy entrepreneurs participate in the market of converting biomass into energy. The term entrepreneur takes on many meanings to many people. The Merriam-Webster definition of entrepreneur is one who organizes, manages, and assumes the risks of a business or enterprise. For the purposes of this chapter, we will use the definition from Shane and Venkataraman (2000), who described entrepreneurship as the process by which “opportunities to create future goods and services are discovered, evaluated, and exploited.”

Although not all entrepreneurs are as successful as the top few, many share the same traits: self-starter, dedicated, risk taker, boundless energy, and vision (University of Maryland Extension). Yet, even with these traits, success is not guaranteed. The bioenergy world is full of successes, failures, and everything in between. The difference between a success and failure in modern bioenergy can come down to timing. The modern corn ethanol industry boomed starting in 2005, with biofuel and environmental policy favoring the blending of ethanol into fuel. Ethanol facilities popped up all over, with some able to pay off debt in as little as 1–2 years, making enormous profits. The facilities that came online toward the end of the decade struggled with high corn prices and low margins (Hofstrand, 2008). Timing, either by luck or vision, is a key.

1.4.1 Predictive instrumentalism

Though evolutionary approaches are in themselves of great value they certainly do not have much punch in defending the assumption of rational economic man as an explanatory figure in economics. It might be argued, though, that the Homo oeconomicus model may be successfully applied to predict the outcomes of evolution. Beyond maximisation one cannot go and therefore the limit of evolutionary processes can be characterised by solving a maximisation problem. If evolution has time to run its course and if appropriate conditions prevail (see however again [Radner, 1998; Smith, 2008, chap. 8]) then the outcomes will be as if chosen by fully rational beings with unlimited computing abilities and the like. Many developments in evolutionary game theory seem to support such a view since, for instance, some of the more complicated and subtle equilibrium selection criteria can be justified in such a setting without relying on empirically outrageous assumptions about common knowledge and reasoning that have to be adopted in eductive game theory (see for instance [Damme, 1987]). But what does this exactly signify?

Since in the preceding Homo oeconomicus behaviour is used as an instrument of prediction the corresponding methodological view may be named “predictive instrumentalism”. Schumpeter characterised the underlying basis of that kind of argument already in the following way:

“The assumption that conduct is prompt and rational is in all cases a fiction. But it proves sufficiently near to reality, if things have time to hammer logic into men. Where this has happened, and within the limits it has happened, one may rest content with this fiction and build theories upon it … and we can depend upon it that the peasant sells his calf just as cunningly and egoistically as the stock exchange member his portfolio of shares. But this holds good only where precedents without number have formed conduct through decades and, in fundamentals, through hundreds and thousands of years, and have eliminated unadapted behaviour. Outside of these limits our fiction loses its closeness to reality”. [Schumpeter, 1959, 80]

According to this point of view the model of rational economic man cannot be used to explain. But it can be used to predict outcomes of long run evolution under sufficiently stable conditions. The assumption that such predictions as made by relying on the economists' maximisation assumption work must, however, itself be justified — i.e. explained — in terms of the evolutionary argument.

An analogous line might obviously carry over to Public Choice. If stable institutional conditions prevail for a sufficiently long time then evolutionary selection may do the trick of “hammering logic” even into political actors. Therefore under such conditions we would have good reason to assume that predictive instrumentalism may have some justification as a tool for Public Choice.

Since the evolutionary explanation of the process tells us why, it would not be a kind of miracle that such a predictive instrument based on cognitively omnipotent Homo oeconomicus works. Nevertheless, it should be noted, first, that this defence of instrumentalism is respectable only insofar as it is itself subject to a deeper explanation of why it does work in evolutionary terms. The true scientific explanation operating in the background is evolutionary and not in terms of the homo oeconomicus behaviour and strategic action. It should be noted, second, that if there is no repetition within a stable institutional framework such as to hammer the logic of rational behaviour by competitive selection into actors the previous argument seizes to be valid. If, as is most likely the case in politics, no appropriate feed back loops such as to select “as if maximizing” rational behaviour do exist the application of the model even for predictive uses loses its basis (and Lichtenberg sends his regards again).

1.4 The impact of innovation

Innovation can be seen as the third step in a process that starts with a scientific discovery, followed by technological invention, commercial innovation, and diffusion (see Figure 1.3). Diffusion is the step that follows innovation and relates to the process and speed in which the new product is adopted by society at large. When an innovation diffuses into society, it will affect the way of life, the more so the wider it has been adopted.

The idea behind this thought model is that new insights and ideas are cascaded to the subsequent stage and have to be processed in the technology and business domains before they can create value in society. This model seriously simplifies reality, but it serves to show that the impact and value of innovation have to be measured by its use in society and not by the efforts in the science and technology domains.

Most innovators will like to believe that their innovation will improve the quality of life and change the world for the better. Therefore, it would be fair to expect that their efforts will not only be rewarded in financial terms but also with warm appreciation. Indeed, innovators are admired for their efforts, but they should be aware that not everybody will be happy because there rarely are winners without losers. Innovation also creates losers and losers tend to be unhappy. The purpose of innovation is creating positive, valuable change with a new, better product that improves the quality of life for a customer. But, unfortunately, innovation always also has a negative element because other existing products and businesses will become less attractive. Sometimes this change is unintentional and can be seen as the collateral damage of innovation.

The threat of negative impact and of undesired change will create resistance to an innovation, and the more radical the innovation is, the more intense the resistance can be. Joseph Schumpeter, who is considered by many as the founder of the theory on innovation, argues that innovation leads to periods of ‘creative destruction’, as innovations cause existing technologies, systems, and equipment to become obsolete.5 Well-known examples of periods of change from history are the transition from sailing boats to steam ships, or the introduction of cars and trains. Resistance did come not only from competitors using the traditional technology but also from sections of society that resisted the potential change in lifestyle.

Resistance to change is not limited to major innovations that have the potential to change the lifestyle; it can also happen to the small ‘improvements of life’. The umbrella, invented by Jonas Hanway in the eighteenth century, was resisted on religious grounds because it interfered with the intentions of the Heavens. The current shift away from the traditional cork for sealing a bottle of wine is not welcomed by all wine lovers even when the new products seal better and are easier to use. The same people will be horrified by the invention that aims to change the taste of wine in a particular way by putting the bottle in a microwave oven for breaking specific nanocapsules that have been added. Attachment to tradition and nostalgic feelings are not the natural supporters of radical innovation.

Change should be seen as an integral part of innovation, and managing the resistance to change is an integral part of managing an innovation process. In change management, the term ‘crossing’ is often used to illustrate the steps that have to be made and the emotive reaction that a change in organization or working practice can bring. Both innovation and change involve crossing a divide, and both are considered risky activities. Innovation, change, and resistance are linked intrinsically: innovation creates change and change creates resistance.

Societal resistance is very dependent on the timing of the innovation. In certain periods, specific types of innovation are welcome and appreciated. For instance, at the beginning of the Industrial Revolution, mechanical innovations were popular; in the first part of the previous century, the development of chemical processes based on hydrocarbons from coal or oil was seen as progress; and in the current emerging digital era, novel ITC applications are welcomed by society at large for shaping new ways of living.

Being in tune with the prevailing value system of society is a major advantage and a key success factor. Technologies develop, mature, fade away, and are replaced by more advanced ones; they can become fashionable or lose their charms. The resistance against nuclear energy is based on a divergence with prevailing values in society; its perceived long-term risks and its association with a destructive, military technology from the past are more dominant than the vision of a constructive technology for the future. These days, new technologies have to be green and sustainable, and a growing part of society sees solar energies as a better technological option for the future.

But an innovator has to be in tune with more developments. Also investors go through cycles of interests and preferences. Asking for money at the wrong time may give an unnecessary negative result. Close contact with venture capital providers and knowing when to ask for money can make the difference between success and unsuccess. All these factors lead to a basic rule in innovation:

– Timing is of the essence

Success in innovation depends on recognizing and taking advantage of the ‘window of opportunity’, with respect to both customers and investors.

However, the other side of the coin of being in tune with societal and technological developments brings the risk that somebody else has a similar idea and is developing a competitive option. Brilliant minds work alike. And the winner may not be the best invention or the first one to market. History teaches that predicting which innovation will prevail is very difficult. Not only technical, commercial, or rational factors play a role, also emotional and irrational considerations are important. The competition between the options for powering a car at the beginning of the previous century may serve as an example. Steam power provided familiar and reliable technology, the electrically powered car was fastest and easy to operate and cars with internal combustion engines were noisy, polluting, and difficult to start, but the latter came out as the ultimate winner. This example illustrates another rule in innovation:

– The winner is unpredictable

Finally, it is important to appreciate the impact that radical innovation has on the bottom line. Contrary to popular belief, radical innovation is not the best way to get rich fast. On the contrary, the benefits of innovation tend to come slowly, and the more radical the innovation is the longer it will take. Radical innovation is a long-term investment for delivering a contribution to the future cash flow of a business, and incremental innovation is the vehicle for creating cash by improving margins in the shorter term.

The lead times of certain type of innovations can be very long. New processes in the chemical industry or medicines from the pharmaceutical industry make take 30 years before they make a genuine impact and contribution to the bottom line. New engines may take a decade or more. On the other hand, popular ITC applications can bring in real money within months. But here the life expectancy of the innovation runs the risk of being relatively short. In the music industry, vinyl LPs were replaced by tapes and later by CDs, which at the time were believed by many to be the final solution, but who buys a CD now? At the end of the twentieth century, the fax was seen as the ultimate communication system in the business world. The benefits of radical innovation to the bottom line tend to be ‘late, but long lasting’ or ‘soon, but short’, depending on the lifecycle of the technology or product.