Innovation, the core renewal process within an organization (as described in Chapter 1), is a generic activity associated with survival and growth. At this level of abstraction, we can see the underlying process as common to all firms. The challenge facing any organization is to find the ways of managing the innovation process to provide a good solution for the problem of renewal or refreshing of the essence, creation, and delivery of a firm’s offerings. At its heart, innovation involves

• Searching Scanning the (internal and external) environment for and processing relevant signals about threats and opportunities for change.
• Selecting Deciding (based on a strategic view of how the enterprise can best develop) which of these signals to respond to.
• Implementing Translating the potential in the trigger idea into something new and launching it in an internal or external market. Making this happen is not a single event but requires eventually acquiring the knowledge resources to enable the innovation, executing the project under conditions of uncertainty (which require extensive problem-solving), and launching the innovation into relevant internal or external markets.
• Capturing value from the innovation In terms of sustaining adoption and diffusion and in learning from progressing through this cycle so that the organization can build its knowledge base and can improve the ways in which the process is managed.

In this chapter, we’ll explore some of the influences on this core process and the different variations on the core innovation theme.

Figure 2.1 reproduces the model of the innovation process that we’ll be using throughout the rest of the book.

2.1 Different Circumstances, Similar Management

Different circumstances lead to many different solutions to the challenge of organizing innovation. For example, large science-based firms such as pharmaceutical companies will tend to create solutions that involve heavy activities around formal R&D, patent searching, and other tasks, while small engineering subcontractors will emphasize rapid implementation capability. Retailers may have relatively small R&D commitments in the formal sense but stress scanning the environment to pick up new consumer trends, and they are likely to place heavy emphasis on marketing.

Consumer goods producers may be more concerned with rapid product development and launch, often with variants and repositioning of basic product concepts. Heavy engineering firms involved in products such as power plants are likely to be design-intensive and critically dependent on project management and systems integration aspects of the implementation phase. Public sector organizations have to configure it to cope with strong external political and regulatory influences.

Despite these variations, the underlying pattern of phases in innovation remains constant. In this chapter, we explore the process nature of innovation in more detail and look at the kinds of variations on this basic theme. But we also want to suggest that there is some commonality around the things that are managed and the influences that can be brought to bear on them in successful innovation. These “enablers” represent the levers that can be used to manage innovation in any organization. Once again, how these enablers are actually put together varies between firms, but they represent particular solutions to the general problem of managing innovation. Exploring these enablers in more detail is the basis of the following chapters in the book.

Central to our view is that innovation management is a learned capability. Although there are common issues to be confronted and a convergent set of recipes for dealing with them, each organization must find its own particular solution and develop this in its own context. Simply copying ideas from elsewhere is not enough; these must be adapted and shaped to suit particular circumstances.

Innovations vary widely in scale, nature, degree of novelty, and so on – and so do innovating organizations. But at this level of abstraction, it is possible to see the same basic process operating in each case. For example, developing a new consumer product will involve picking up signals about potential needs and new technological possibilities, developing a strategic concept, coming up with options, and then working those up into new products, which can be launched into the marketplace.

In a similar fashion, choosing to install a new piece of process technology also follows this pattern. Signals about needs – in this case, internal ones, such as problems with the current equipment – and new technological means are processed and provide an input to developing a strategic concept. This then requires identifying an existing option, or inventing a new one, which must then be developed to such a point that it can be implemented, that is, launched, by users within the enterprise – effectively by a group of internal customers. The same principles of needing to understand their needs and to prepare the marketplace for effective launch will apply as in the case of product innovation.

2.2 Services and Innovation

Competitive advantage undoubtedly can come from innovation in services – as we can see in the “Services and Innovation Management” box (Box 2.1) [1]. It is worth reflecting that the world’s first business computer was applied in the service sector (see Case Study 2.1).

Citibank was the first bank to offer automated telling machinery (ATM) service and developed a strong market position as a technology leader on the back of this process innovation, while Bank of America is literally a textbook case of service innovation via experimentation with new technologies and organizational arrangements across its branch network. Companies such as Benneton and Zara owe much of their success to sophisticated information technology (IT)-led design and production networks (“fast fashion”), which they have innovated over decades.

Southwest Airlines achieved an enviable position as the most effective airline in the United States despite being much smaller than its rivals; its success was due to process innovation in areas such as reduction of airport turnaround times. This model has subsequently become the template for a whole new generation of low-cost airlines whose efforts have revolutionized the once-cozy world of air travel. And in the worlds of insurance, legal services, and banking, there is plenty of scope for innovation, both from existing players renewing their approaches (e.g., First Direct as a long-standing success story in the United Kingdom, coming out of HSBC Bank) and from entrepreneurial approaches such as Zopa (opening up peer-to-peer lending). In fact, as Case Study 2.1 shows, it was in the service sector that computers were first applied in business.

Importantly, we need to remember that the advantages that flow from these innovative steps gradually get competed away as others imitate. Unless an organization is able to move into further innovation, it risks being left behind as others take the lead in changing their offerings, their operational processes, or the underlying models that drive their business. For example, leadership in banking has been passed on to others, particularly those who were able to capitalize early on the boom in information and communications technologies; particularly, many of the lucrative financial services such as securities and shares dealing became dominated by players with radical new models like Charles Schwab [2]. As retailers all adopt advanced IT, so the lead shifts to those who are able – like Zara and Benneton – to streamline their production operations to respond rapidly to the signals flagged by the IT systems.

With the rise of the Internet, the scope for service innovation has grown enormously – not for nothing is it sometimes called “a solution looking for problems.” As Evans and Wurster point out, the traditional picture of services being offered, either as a standard to a large market (high “reach” in their terms) or else highly specialized and customized to a particular individual able to pay a high price (high “richness”), is “blown to bits” by the opportunities of Web-based technology [3]. Now it becomes possible to offer both richness and reach at the same time – and thus to create totally new markets and disrupt radically those that exist in any information-related businesses.

Table 2.1 gives some examples of different types of innovation in services, using the same “4Ps” typology, which we introduced in Chapter 1.

Service innovations are often much easier to imitate, and the competitive advantages that they offer can quickly lose ground because there are fewer barriers – for example, of intellectual property (IP) protection. The pattern of airline innovation on the transatlantic route provides a good example of this – there is a fast pace of innovation, but as soon as one airline introduces something like a flat bed, others will quickly emulate it. Arguably, the drive to personalization of the service experience will be strong because it is only through such customized experiences that a degree of customer “lock-on” takes place [4]. Certainly, the experience of mobile phones, Internet banking, and insurance suggests that, despite attempts to customize the experience via sophisticated Web technologies, there is little customer loyalty and a high rate of churn. However, the lower capital cost of creating and delivering services and their relative simplicity make cocreation more of an option. Where manufacturing may require sophisticated tools such as computer-aided design and rapid prototyping, services lend themselves to shared experimentation at relatively lower cost. There is growing interest in such models involving active users in design of services – for example, in the open-source movement around software or in the digital entertainment and communication fields where community and social networking sites such as Facebook, Instagram, WhatsApp, and YouTube have had a major impact. Research Note 2.1 discusses another area of interest: experience innovation.

2.3 Variations on a Theme

One of the significant developments in business innovation, driven by globalization and enabling technologies, has been the “outsourcing” of key business processes – IT, call center management, human resources administration, and so on. Although indicative of a structural shift in the economy, it has, at its heart, the same innovation drivers. In addition, the distinction between commercial and not-for-profit organizations may also blur when considering innovation. While private sector firms may compete for the attentions of their markets by offering new things or new ways of delivering them, public sector and nonprofit organizations use innovation to help them compete against the challenges of delivering healthcare, education, law and order, and so on [10].

Do Better/Do Different

It’s not just the sector, size of firm, or wider context that moderates the way the innovation process operates. An increasing number of authors draw attention to the need to take the degree of novelty in an innovation into account [35–37]. At a basic level, the structures and behaviors needed to help enable incremental improvements will tend to be incorporated into the day-to-day standard operating procedures of the organization. More radical projects may require more specialized attention – for example, arrangements to enable working across functional boundaries. At the limit, the organization may need to review the whole bundle of routines that it uses for managing innovation when it confronts discontinuous conditions and the “rules of the game” change.

As we saw in Chapter 1, we can think of innovation in terms of two complementary modes. The first can be termed “doing what we do but better”– a “steady state” in which innovation happens but within a defined envelope around which our “good practice” routines can operate. This contrasts with “do different” innovation where the rules of the game have shifted (due to major technological, market, or political shifts, for example) and where managing innovation is much more a process of exploration and coevolution under conditions of high uncertainty. A number of writers have explored this issue and conclude that, under turbulent conditions, firms need to develop capabilities for managing both aspects of innovation [38–40].

Once again, the generic model of the innovation process remains the same. Under “do different” conditions, organizations still need to search for trigger signals – the difference is that they need to explore in much less familiar places and deploy peripheral vision to pick up weak signals early enough to move. They still need to make strategic choices about what they will do – but they will often have vague and incomplete information, and the decision-making involved will thus be much more risky – arguing for a higher tolerance of failure and fast learning. Implementation will require much higher levels of flexibility around projects – and monitoring and review may need to take place against more flexible criteria than might be applied to “do better” innovation types [41].

For established organizations, the challenge is that they need to develop the capability to manage both kinds of innovation. Much of the time, they will need robust systems for dealing with “do better,” but from time to time, they risk being challenged by new entrants better able to capitalize on the new conditions opened up by discontinuity – unless they can develop a “do different” capability to run in parallel. New entrants don’t have this problem when riding the waves of a discontinuous shift – for example, exploiting opportunities opened up by a completely new technology. But they, in turn, will become established incumbents and face the challenge later if they do not develop the capacity to exploit their initial advantage through “do better” innovation process and also build capability for dealing with the next wave of change by creating a “do different” capability [42].

Table 2.4 highlights the differences between these two ways of thinking and operating.

Example	Type 1 – Steady-state Archetype	Type 2 – Discontinuous-innovation Archetype
Interpretive schema – how the organization sees and makes sense of the world	There is an established set of “rules of the game” by which other competitors also play Particular pathways in terms of search and selection environments and technological trajectories exist and define the “innovation space” available to all players in the game	No clear “rules of the game” – these emerge over time but cannot be predicted in advance Need high tolerance for ambiguity – seeing multiple parallel possible trajectories
Strategic decision-making	Strategic direction is highly path-dependent Makes use of decision-making processes, which allocate resources on the basis of risk management linked to the aforementioned “rules of the game” (Does the proposal fit the business strategic directions? Does it build on existing competence base?) Controlled risks are taken within the bounds of the “innovation space” Political coalitions are significant influences maintaining the current trajectory	“Innovation space” defined by open and fuzzy selection environment. Probe and learn experiments needed to build information about emerging patterns and allow dominant design to emerge Highly path-independent High levels of risk taking since no clear trajectories – emphasis on fast and lightweight decisions rather than heavy commitment in initial stages Multiple parallel bets, fast failure and learning as dominant themes. High tolerance of failure, but risk is managed by limited commitment. Influence flows to those prepared to “stick their neck out” – entrepreneurial behavior
Operating routines	Operates with a set of routines and structures/procedures that embed them, which are linked to these “risk rules” – for example, stage gate monitoring and review for project management Search behavior is along defined trajectories and uses tools and techniques for R&D, market research, and so on, which assume a known space to be explored – search and selection environment Network building to support innovation – for example, user involvement, supplier partnership, and so on – is on basis of developing close and strong ties	Operating routines are open-ended, based around managing emergence Project implementation is about “fuzzy front end,” light touch strategic review, and parallel experimentation Probe and learn, fast failure and learn rather than managed risk Search behavior is about peripheral vision, picking up early warning through weak signals of emerging trends Linkages are with heterogeneous population and emphasis less on established relationships than on weak ties

The challenge is thus – as shown in Figure 2.2 – to develop an ambidextrous capability for managing both kinds of innovation within the same organization. We will return to this theme repeatedly in the book, exploring the additional or different challenges posed when innovation has to be managed beyond the steady state.

2.4 A Contingency Model of the Innovation Process

Table 2.5 lists some of the wide range of influences around which organizations need to configure their particular versions of the generic innovation process. The key message in this section is that the same generic process can be observed – the management challenge is configuration.

2.5 Evolving Models of the Process

The importance of viewing innovation as a process is that this understanding shapes the way in which we try and manage it. Put simply, our mental models shape our actions – we pay attention to, allocate resources to, take decisions about things according to how we think about them. So, if innovation is a process, we need to have a clear and shared understanding of what that process involves and how it operates.

This understanding of the core process model has changed a great deal over time. Early models (both explicit and, more important, the implicit mental models whereby people managed the process) saw it as a linear sequence of functional activities. Either new opportunities arising out of research gave rise to applications and refinements, which eventually found their way to the marketplace (“technology push”) or else the market signaled needs for something new, which then drew through new solutions to the problem (“need pull,” where necessity becomes the mother of invention).

The limitations of such an approach are clear; in practice, innovation is a coupling and matching process where interaction is the critical element [59,60]. Sometimes, the “push” will dominate, sometimes the “pull,” but successful innovation requires interaction between the two. The analogy to a pair of scissors is useful here; without both blades, it is difficult to cut. (Chapter 5 explores the issue of sources of innovation and how there is considerable interplay between these two types.)

One of the key problems in managing innovation is that we need to make sense of a complex, uncertain, and highly risky set of phenomena. Inevitably, we try and simplify these through the use of mental models – often reverting to the simplest linear models to help us explore the management issues that emerge over time. Prescriptions for structuring the process along these lines abound; for example, one of the most cited models for product innovation was developed by the consultants Booz, Allen, and Hamilton [61]. Many variations exist on this theme – for example, Robert Cooper’s work suggests a slightly extended view with “gates” between stages, which permit management of the risks in the process [62]. There is also a British Standard (BS 7000) that sets out a design-centered model of the process [63].

Much recent work recognizes the limits of linear models and tries to build more complexity and interaction into the frameworks. For example, the Product Development Management Association (PDMA) offers a detailed guide to the process and an accompanying toolkit [64]. Increasingly, there is recognition of some of the difficulties around what is often termed the “fuzzy front end” where uncertainty is the highest, but there is still convergence around a basic process structure as a way of focusing our attention [65]. The balance needs to be struck between simplifications and representations that help thinking – but just as the map is not the same as the territory it represents, so they need to be seen as frameworks for thinking, not as descriptions of the way the process actually operates.

Most innovation is messy, involving false starts, recycling between stages, dead ends, jumps out of sequence, and so on. Various authors have tried different metaphors – for example, seeing the process as a railway journey with the option of stopping at different stations, going into sidings or even, at times, going backward – but most agree that there is still some sequence to the basic process [66,67]. In an important program of case-study-based research looking at widely different innovation types, Andrew Van de Ven and colleagues explored the limitations of simple models of the process [68]. They drew attention to the complex ways in which innovations actually evolve over time and derived some important modifiers to the basic model:

• Shocks trigger innovations – change happens when people or organizations reach a threshold of opportunity or dissatisfaction.
• Ideas proliferate – after starting out in a single direction, the process proliferates into multiple, divergent progressions.
• Setbacks frequently arise, plans are overoptimistic, commitments escalate, mistakes accumulate, and vicious cycles can develop.
• Restructuring of the innovating unit often occurs through external intervention, personnel changes, or other unexpected events.
• Top management plays a key role in sponsoring – but also in criticizing and shaping – innovation.
• Success criteria shift over time, differ between groups, and make innovation a political process.
• Innovation involves learning, but many of its outcomes are due to other events that occur as the innovation develops – making learning often “superstitious” in nature.

They suggest that the underlying structure can be represented by the metaphor of an “innovation journey,” which has key phases of initiation, development, and implementation/termination. But the progress of any particular innovation along this will depend on a variety of contingent circumstances; depending on which of these apply, different specific models of the process will emerge.

Roy Rothwell was, for many years, a key researcher in the field of innovation management, working at SPRU at the University of Sussex. In one of his later papers, he provided a useful historical perspective on this, suggesting that our appreciation of the nature of the innovation process has been evolving from such simple linear models (characteristic of the 1960s) through to increasingly complex interactive models (Table 2.4). His “fifth-generation innovation” concept sees innovation as a multiactor process, which requires high levels of integration at both intra- and interfirm levels and which is increasingly facilitated by IT-based networking [69]. While his work did not explicitly mention the Internet, it is clear that the kinds of innovation management challenge posed by the emergence of this new form fit well with the model. Although such fifth-generation models and the technologies that enable them appear complex, they still involve the same basic process framework [70].

In essence, we are talking about “innovation model innovation” – changing and revising our internal representations of how innovation happens and adapting these to take account of shifts in enabling technologies, social and legal frameworks, and market conditions. The shift to “open innovation” – which we discuss in more detail in Chapter 11 – represents a good example, fleshing out Rothwell’s fifth-generation model into one based on open and collective innovation [71]. And there is growing discussion about the implications for innovation models based on “open user innovation” [72].

Table 2.6 illustrates Rothwell’s five generations.

Mental models are important because they help us frame the issues that need managing – but therein also lies the risk. If our mental models are limited, then our approach to managing is also likely to be limited. For example, if we believe that innovation is simply a matter of coming up with a good invention – then we risk managing that part of the process well, but failing to consider or deal with other key issues around actually taking that invention through technological and market development to successful adoption.

Here are some examples of the problems in “partial thinking”:

• Seeing innovation as a linear “technology push” process (in which case all the attention goes into funding R&D with little input from users) or one in which the market can be relied upon to pull through innovation.
• Seeing innovation simply in terms of major “breakthroughs” – and ignoring the significant potential of incremental innovation. In the case of electric light bulbs, the original Edison design remained almost unchanged in concept, but incremental product and process improvement over the 16 years from 1880 to 1896 led to a fall in price of around 80% [74].
• Seeing innovation as a single isolated change rather than as part of a wider system (effectively restricting innovation to component level rather than seeing the bigger potential of architectural changes) [75].
• Seeing innovation as product or process only, without recognizing the interrelationship between the two.

Table 2.7 provides an overview of the difficulties that arise if we take a partial view of innovation.

2.6 Can We Manage Innovation?

It would be hard to find anyone prepared to argue against the view that innovation is important and likely to be more so in the coming years. But that still leaves us with the big question of whether or not we can actually manage what is clearly an enormously complex and uncertain process.

There is certainly no easy recipe for success. Indeed, at first glance, it might appear that it is impossible to manage something so complex and uncertain. There are problems in developing and refining new basic knowledge, problems in adapting and applying it to new products and processes, problems in convincing others to support and adopt the innovation, problems in gaining acceptance and long-term use, and so on. Since so many people with different disciplinary backgrounds, varying responsibilities and basic goals are involved, the scope for differences of opinion and conflicts over ends and means is wide. In many ways, the innovation process represents the place where Murphy and his associated band of lawmakers hold sway, where if anything can go wrong, there’s a very good chance that it will!

But despite the uncertain and apparently random nature of the innovation process, it is possible to find an underlying pattern of success. Not every innovation fails, and some firms (and individuals) appear to have learned ways of responding and managing it such that, while there is never a cast-iron guarantee, at least the odds in favor of successful innovation can be improved. We are using the term “manage” here not in the sense of designing and running a complex but predictable mechanism (such as an elaborate clock) but rather that we are creating conditions within an organization under which a successful resolution of multiple challenges under high levels of uncertainty is made more likely.

One indicator of the possibility of doing this comes from the experiences of organizations that have survived for an extended period of time. While most organizations have comparatively modest lifespans, there are some that have survived at least one and sometimes multiple centuries. Looking at the experience of these “100 club” members – firms such as 3M, Corning, Procter & Gamble, Reuters, Siemens, Philips, and Rolls-Royce – we can see that much of their longevity is down to having developed a capacity to innovate on a continuing basis. They have learned – often the hard way – how to manage the process (both in its “do better” and “do different” variants) so that they can sustain innovation [76–78].

It is important to note the distinction here between “management” and managers. We are not arguing here about who is involved in taking decisions or directing activity, but rather about what has to be done. Innovation is a management question, in the sense that there are choices to be made about resources and their disposition and co-ordination. Close analysis of many technological innovations over the years reveals that although there are technical difficulties – bugs to fix, teething troubles to be resolved, and the occasional major technical barrier to surmount – the majority of failures are due to some weakness in the way the process is managed. Success in innovation appears to depend upon two key ingredients – technical resources (people, equipment, knowledge, money, etc.) and the capabilities in the organization to manage them.

This brings us to the concept of what have been termed “routines” [79]. Organizations develop particular ways of behaving, which become “the way we do things around here” as a result of repetition and reinforcement. These patterns reflect an underlying set of shared beliefs about the world and how to deal with it and form part of the organization’s culture – “the way we do things in this organization.” They emerge as a result of repeated experiments and experience around what appears to work well – in other words, they are learned. Over time, the pattern becomes more of an automatic response to particular situations, and the behavior becomes what can be termed a “routine.”

This does not mean that it is necessarily repetitive, only that its execution does not require detailed conscious thought. The analogy can be made with driving a car; it is possible to drive along a stretch of motorway while simultaneously talking to someone else, eating or drinking, listening to, and concentrating on, something on the radio or planning what to say at the forthcoming meeting. But driving is not a passive behavior; it requires continuous assessment and adaptation of responses in the light of other traffic behavior, road conditions, weather, and a host of different and unplanned factors. We can say that driving represents a behavioral routine in that it has been learned to the point of being largely automatic.

In the same way, an organizational routine might exist around how projects are managed or new products researched. For example, project management involves a complex set of activities such as planning, team selection, monitoring and execution of tasks, replanning, coping with unexpected crises, and so on. All of these have to be integrated – and offer plenty of opportunities for making mistakes. Project management is widely recognized as an organizational skill, which experienced firms have developed to a high degree but which beginners can make a mess of. Firms with good project management routines are able to codify and pass them on to others via procedures and systems. Most importantly, the principles are also transmitted into “the way we run projects around here” by existing members passing on the underlying beliefs about project management behavior to new recruits.

Over time, organizational behavior routines create and are reinforced by various kinds of artifacts – formal and informal structures, procedures, and processes that describe “the way we do things around here” and symbols that represent and characterize the underlying routines. It could be in the form of a policy – for example, 3M is widely known for its routines for regular and fast product innovation. They have enshrined a set of behaviors around encouraging experimentation into what they term “the 15% policy” in which employees are enabled to work on their own curiosity-driven agenda for up to 15% of their time [80]. These routines are firm-specific – for example, they result from an environment in which the costs of product development experimentation are often quite low.

Levitt and March describe routines as involving established sequences of actions for undertaking tasks enshrined in a mixture of technologies, formal procedures or strategies, and informal conventions or habits [81]. Importantly, routines are seen as evolving in the light of experience that works – they become the mechanisms that “transmit the lessons of history.” In this sense, routines have an existence independent of particular personnel – new members of the organization learn them on arrival, and most routines survive the departure of individual routines. Equally, they are constantly being adapted and interpreted such that formal policy may not always reflect the current nature of the routine – as Augsdorfer points out in the case of 3M [82].

For our purposes, the important thing to note is that routines are what makes one organization different from another in how they carry out the same basic activity. We could almost say they represent the particular “personality” of the firm. Each enterprise learns its own particular “way we do things around here” in answer to the same generic questions – how it manages quality, how it manages people, and so on. The set of routines that describe and differentiate the responses that organizations make to the question of structuring and managing the generic model, which we have been looking at in this chapter (see Figure 2.1), provide a description of “how we manage innovation around here.”

It follows that some routines are better than others in coping with the uncertainties of the outside world, in both the short and the long term. And it is possible to learn from others’ experience in this way; the important point is to remember that routines are firm-specific and must be learned. Simply copying what someone else does is unlikely to help, any more than watching someone drive and then attempting to copy them will make a novice into an experienced driver. There may be helpful clues, which can be used to improve the novice’s routines, but there is no substitute for the long and experience-based process of learning. Box 2.2 gives some examples where change has been introduced without this learning perspective.

2.7 Learning to Manage Innovation

Our argument in this book is that successful innovation management is primarily about building and improving effective routines. Learning to do this comes from recognizing and understanding effective routines (whether developed in-house or observed in another enterprise) and facilitating their emergence across the organization. And this learning process implies a building up of capability over time.

It’s easy to make the assumption that because there is a rich environment full of potential sources of innovation that every organization will find and make use of these. The reality is, of course, that they differ widely in their ability to innovate – and this capability is clearly not evenly distributed across a population. For example, some organizations may simply be unaware of the need to change, never mind having the capability to manage such change. Such firms (and this is a classic problem of small firm growth) differ from those that recognize in some strategic way the need to change, to acquire and use new knowledge but lack the capability to target their search, or to assimilate and make effective use of new knowledge once identified. Others may be clear about what they need but lack the capability in finding and acquiring it. And others may have well-developed routines for dealing with all of these issues and represent resources on which less experienced firms might draw – as is the case with some major supply chains focused around a core central player [99].

We can imagine a simple typology (see Figure 2.3), ranging from organizations that are “unconsciously ignorant” (they don’t know that they don’t know) through to high-performing knowledge-based enterprises. The distinguishing feature is their capability to organize and manage the innovation process in its entirety, from search through selection to effective implementation of new knowledge. Such capability is not a matter of getting lucky once but of having an embedded high order set of learning routines.

2.8 What Do We Know About Successful Innovation Management?

The good news is that there is a knowledge base on which to draw in attempting to answer this question. Quite apart from the wealth of experience (of success and failure) reported by organizations involved with innovation, there is a growing pool of knowledge derived from research. Over the past 80 years or so, there have been many studies of the innovation process, looking at many different angles. Different innovations, different sectors, firms of different shapes and sizes, operating in different countries, and so on, have all come under the microscope and been analyzed in a variety of ways. (Chapter 10 provides a detailed list of such studies.)

From this knowledge base, it is clear that there are no easy answers and that innovation varies enormously – by scale, type, sector, and so on. Nonetheless, there does appear to be some convergence around our two key points:

• Innovation is a process, not a single event, and needs to be managed as such.
• The influences on the process can be manipulated to affect the outcome – that is, it can be managed.

Most importantly, research highlights the concept of success routines, which are learned over time and through experience. For example, successful innovation correlates strongly with how a firm selects and manages projects, how it co-ordinates the inputs of different functions, how it links up with its customers, and so on. Developing an integrated set of routines is strongly associated with successful innovation management and can give rise to distinctive competitive ability – for example, being able to introduce new products faster than anyone else or being able to use new process technology better [106–108].

The other critical point to emerge from research is that innovation needs managing in an integrated way; it is not enough just to manage or develop abilities in some of these areas. One metaphor (originally developed by researchers at Cranfield University) that helps draw attention to this is to see managing the process in sporting terms; success is more akin to winning a multievent group of activities (such as the pentathlon) than to winning a single high-performance event such as the 100 meters race [109].

There are many examples of firms that have highly developed abilities for managing part of the innovation process but that fail because of a lack of ability in others. For example, there are many with an acknowledged strength in R&D and the generation of technological innovation – but which lack the abilities to relate these to the marketplace or to end users. Others may lack the ability to link innovation to their business strategy. For example, many firms invested in advanced manufacturing technologies – robots, computer-aided design, computer-controlled machines, and so on – during the late twentieth century, but most surveys suggest that only half of these investments really paid off. In the case of the other half, the problem was an inability to match the “gee whiz” nature of a glamorous technology to their particular needs, and the result was what might be called “technological jewelry”– visually impressive but with little more than a decorative function.

The concept of capability in innovation management also raises the question of how it is developed over time. This must involve a learning process. It is not sufficient to simply have experiences (good or bad); the key lies in evaluating and reflecting upon them and then developing the organization in such a way that the next time a similar challenge emerges, the response is ready. Such a cycle of learning is easy to prescribe but very often missing in organizations – with the result that there often seems to be a great deal of repetition in the pattern of mistakes and a failure to learn from the misfortunes of others. For example, there is often no identifiable point in the innovation process where a postmortem is carried out, taking time to try and distil useful learning for next time. In part, this is because the people involved are too busy, but it is also because of a fear of blame and criticism. Yet, without this pause for thought, the odds are that the same mistakes will be repeated [109,110]. It’s important to note that even “good” innovation management organizations can lose their touch – for example, 3M, for many years, a textbook case of how to manage the process found itself in difficulties as a result of overemphasis on incremental innovation (driven by a “Six Sigma” culture) at the expense of “breakthrough” thinking. Its reflection on the problems this posed and commitment to reshaping its innovation management agenda again underlines the importance of learning and of the idea of “dynamic capability.” (We will return to this theme in Chapter 15.)

View 2.1 box gives some examples of the key success factors in innovation as seen by practicing innovation managers.

Success Routines in Innovation Management

Successful innovators acquire and accumulate technical resources and managerial capabilities over time; there are plenty of opportunities for learning – through doing, using, working with other firms, asking the customers, and so on – but they all depend upon the readiness of the firm to see innovation less as a lottery than as a process, which can be continuously improved.

From the various studies of success and failure in innovation, it is possible to construct checklists and even crude blueprints for effective innovation management. A number of models for auditing innovation have been developed in recent years, which provide a framework against which to assess performance in innovation management. Some of these involve simple checklists, others deal with structures, others with the operation of particular subprocesses [112–114]. (We will return to the theme of innovation audits and their role in helping develop capability in Chapter 15.)

For our purposes in exploring innovation management throughout the rest of the book, it will be helpful to build on our own simple model (Figure 2.4) and use it to focus attention on key aspects of the innovation management challenge. At its heart, we have the generic process described earlier, which sees innovation as a core set of activities distributed over time. (Of course, as we noted earlier, innovation in real life does not conform neatly to this simple representation – and it is rarely a single event but rather a cycle of activities repeated over time.) The key point is that a number of different actions need to take place as we move through the phases of this model and associated with each are some consistent lessons about effective innovation management routines.

Search

The first phase in innovation involves detecting signals in the environment about potential for change. These could take the form of new technological opportunities or changing requirements on the part of markets; they could be the result of legislative pressure or competitor action. Most innovations result from the interplay of several forces, some coming from the need for change pulling through innovation, and others from the push that comes from new opportunities.

Given the wide range of signals, it is important for successful innovation management to have well-developed mechanisms for identifying, processing, and selecting information from this turbulent environment. Chapter 6 explores enabling routines associated with successful scanning and processing of relevant signals.

Organizations don’t, of course, search in infinite space but rather in places where they expect to find something helpful. Over time, their search patterns become highly focused and this can – as we have seen – sometimes represent a barrier to more radical forms of innovation. A key challenge in innovation management relates to the clear understanding of what factors shape the “selection environment” and the development of strategies to ensure that their boundaries of this are stretched. Again, this theme is picked up in Chapter 6.

Selection

Innovation is inherently risky, and even well-endowed organizations cannot take unlimited risks. It is thus essential that some selection is made of the various market and technological opportunities and that the choices made fit with the overall business strategy of the firm and build upon established areas of technical and marketing competence. The purpose of this phase is to resolve the inputs into an innovation concept, which can be progressed further through the development organization.

Three inputs feed this phase (Figure 2.5). The first is the flow of signals about possible technological and market opportunities available to the enterprise. The second input concerns the current knowledge base of the organization – its distinctive competence [114]. By this, we mean what it knows about terms of its product or service and how that is produced or delivered effectively. This knowledge may be embodied in particular products or equipment, but is also present in the people and systems needed to make the processes work. The important thing here is to ensure that there is a good fit between what the organization currently knows about and the proposed changes it wants to make.

This is not to say that organizations should not move into new areas of competence; indeed, there has to be an element of change if there is to be any learning. But rather, there needs to be a balance and a development strategy. This raises the third input to this phase – the fit with the overall business. At the concept stage, it should be possible to relate the proposed innovation to improvements in overall business performance. Thus, if a firm is considering investing in flexible manufacturing equipment because the business is moving into markets where increased customer choice is likely be critical, it will make sense. But if it is doing so in a commodity business where everyone wants exactly the same product at the lowest price, then the proposed innovation will not underpin the strategy – and will effectively be a waste of money. Getting close alignment between the overall strategy for the business and the innovation strategy is critical at this stage.

In a similar fashion, many studies have shown that product innovation failure is often caused by firms trying to launch products that do not match their competence base [64].

This knowledge base need not be contained within the firm; it is also possible to build upon competencies held elsewhere. The requirement here is to develop the relationships needed to access the necessary complementary knowledge, equipment, resources, and so on. Strategic advantage comes when a firm can mobilize a set of internal and external competencies – what Teece calls “complementary assets” – which make it difficult for others to copy or enter the market [114]. (This theme is picked up in more depth in Chapter 4, and Chapters 7 and 8 explore in more detail some of the key routines associated with managing the strategic selection of innovation projects and building a coherent and robust portfolio.)

While the aforementioned discussion has focused particularly on business innovators, we can see similar patterns in public sector and not-for-profit innovation. Once again, the questions about core knowledge are critical. For example, the World Food Programme of the United Nations (one of the key mechanisms for providing humanitarian food assistance) has fundamentally changed its model form sourcing and distributing food toward giving people money with which to procure their own resources. This significant shift required a whole new set of skills and knowledge, effectively building a banking and financial management system to go alongside their accumulated expertise in logistics and distribution. They achieved this through a strategic partnership with MasterCard.

Implementation

Having picked up relevant trigger signals and made a strategic decision to pursue some of them, the next key phase is actually turning those potential ideas into some kind of reality – a new product or service, a change in process, a shift in business model, and so on. In some ways, this implementation phase can be seen as one that gradually pulls together different pieces of knowledge and weaves them into an innovation. At the early stages, there is high uncertainty – details of technological feasibility, of market demand, of competitor behavior, of regulatory and other influences, and so on – all of these are scarce, and strategic selection has to be based on a series of “best guesses.” But gradually over the implementation phase, this uncertainty is replaced by knowledge acquired through various routes and at an increasing cost. Technological and market research helps clarify whether or not the innovation is technically possible or if there is a demand for it and, if so, what are its characteristics. As the innovation develops, a continuing thread of problem-finding and solving – getting the bugs out of the original concept – takes place, gradually building up relevant knowledge around the innovation. Eventually, it is in a form that can be launched into its intended context – internal or external market – and then further knowledge about its adoption (or otherwise) can be used to refine the innovation. Figure 2.6 illustrates this relationship.

We can explore the implementation phase in a little more detail by considering three core elements – acquiring knowledge, executing the project, and launching and sustaining the innovation. Acquiring knowledge involves combining new and existing knowledge (available within and outside the organization) to offer a solution to the problem. It involves both generation of technological knowledge (via R&D carried out within and outside the organization) and technology transfer (between internal sources or from external sources). As such, it represents a first draft of a solution and is likely to change considerably in its development. The output of this stage in the process is both forward to the next stage of detailed development and back to the concept stage where it may be abandoned, revised, or approved.

Much depends, at this stage, on the nature of the new concept. If it involves an incremental modification to an existing design, there will be little activity within the invention stage. By contrast, if the concept involves a totally new concept, there is considerable scope for creativity. While individuals may differ in terms of their preferred creative style, there is strong evidence to support the view that everyone has the latent capability for creative problem-solving [115,116]. Unfortunately, a variety of individual inhibitions and external social and environmental pressures combine and accumulate over time to place restrictions on the exercise of this creative potential. The issue in managing this stage is thus to create the conditions under which this can flourish and contribute to effective innovation.

Another problem with this phase is the need to balance the open-ended environmental conditions that support creative behavior with the somewhat harsher realities involved elsewhere in the innovation process. As with concept testing and development, it is worth spending time exploring ideas and potential solutions rather than jumping on the first apparently workable option.

The challenge in effective R&D is not simply one of putting resources into the system; it is how those resources are used. Effective management of R&D requires a number of organizational routines, including clear strategic direction, effective communication and “buy-in” to that direction, and integration of effort across different groups.

But not all firms can afford to invest in R&D; for many smaller firms, the challenge is to find ways of using technology generated by others or to complement internally generated core technologies with a wider set drawn from outside. This places emphasis on the strategy system discussed earlier – the need to know which to carry out where and the need for a framework to guide policy in this area. Firms can survive even with no in-house capability to generate technology – but to do so, they need to have a well-developed network of external sources, which can supply it, and the ability to put that externally acquired technology to effective use.

It also requires abilities in finding, selecting, and transferring technology in from outside the firm. This is rarely a simple shopping transaction, although it is often treated as such; it involves abilities in selecting, negotiating, and appropriating the benefits from such technology transfer [117].

• Executing the project forms the heart of the innovation process. Its inputs are a clear strategic concept and some initial ideas for realizing the concept. Its outputs are both a developed innovation and a prepared market (internal or external), ready for final launch. This is fundamentally a challenge in project management under uncertain conditions. As we will see in Chapter 9, the issue is not simply one of ensuring that certain activities are completed in a particular sequence and delivered against a time and cost budget. The lack of knowledge at the outset and the changing picture as new knowledge is brought in during development means that a high degree of flexibility is required in terms of overall aims and subsidiary activities and sequencing. Much of the process is about weaving together different knowledge sets coming from groups and individuals with widely different functional and disciplinary backgrounds. And the project may involve groups that are widely distributed in organizational and geographical terms – often belonging to completely separate organizations. Consequently, the building and managing of a project team, of communicating a clear vision and project plan, of maintaining momentum and motivation, and so on, are not trivial tasks.
  
  It is during this stage that most of the time, costs, and commitment are incurred, and it is characterized by a series of problem-solving loops dealing with expected and unexpected difficulties in the technical and market areas. Although we can represent it as a parallel process, in practice, effective management of this stage requires close interaction between marketing-related and technical activities. For example, product development involves a number of functions, ranging from marketing, through design and development to manufacturing, quality assurance, and finally back to marketing. Differences in the tasks that each of these functions performs, in the training and experience of those working there, and in the timescales and operating pressures under which they work all mean that each of these areas becomes characterized by a different working culture. Functional divisions of this kind are often exaggerated by location, where R&D and design activities are grouped away from the mainstream production and sales operations – in some cases, on a completely different site.

  Separation of this kind can lead to a number of problems in the overall development process. Distancing the design function from the marketplace can lead to inappropriate designs, which do not meet the real customer needs, or which are “overengineered,” embodying a technically sophisticated and elegant solution, which exceeds the actual requirement (and may be too expensive as a consequence). This kind of phenomenon is often found in industries that have a tradition of defense contracting, where work has been carried out on a cost-plus basis involving projects that have emphasized technical design features rather than commercial or manufacturability criteria.

  Similarly, the absence of a close link with manufacturing means that much of the information about the basic “make-ability” of a new design either does not get back to the design area at all or else does so at a stage too late to make a difference or to allow the design to be changed. There are many cases in which manufacturing has wrestled with the problem of making or assembling a product that requires complex manipulation, but where minor design change – for example, relocation of a screw hole – would considerably simplify the process. In many cases, such an approach has led to major reductions in the number of operations necessary – simplifying the process and often, as an extension, making it more susceptible to automation and further improvements in control, quality, and throughput.

  In a similar fashion, many process innovations fail because of a lack of involvement on the part of users and others likely to be affected by the innovation. For example, many IT systems, while technically capable, fail to contribute to improved performance because of inadequate consideration of current working patterns, which they will disrupt, lack of skills development among those who will be using them, inadequately specified user needs, and so on.

  Although services are often less tangible, the underlying difficulties in implementation are similar. Different knowledge sets need to be brought together at key points in the process of creating and deploying new offerings. For example, developing a new insurance or financial service product requires technical input on the part of actuaries, accountants, IT specialists, and so on – but this needs to be combined with information about customers and key elements of the marketing mix – the presentation, the pricing, the positioning, and so on, of the new service. Knowledge of this kind will lie particularly with marketing and related staff – but their perspective must be brought to bear early enough in the process to avoid creating a new service that no one actually wants to buy.

  The “traditional” approach to this stage was a linear sequence of problem-solving, but much recent work in improving development performance (especially in compressing the time required) involves attempts to do much of this concurrently or in overlapping stages. Useful metaphors for these two approaches are the relay race and the rugby team [118]. These should be seen as representing two poles of a continuum; as we shall see in Chapter 10, the important issue is to choose an appropriate level of parallel development.

  In parallel with the technical problem-solving associated with developing an innovation, there is also a set of activities associated with preparing the market into which it will be launched. Whether this market is a group of retail consumers or a set of internal users of a new process, the same requirement exists for developing and preparing this market for launch, since it is only when the target market makes the decision to adopt the innovation that the whole innovation process is completed. The process is again one of sequentially collecting information, solving problems, and focusing efforts toward a final launch. In particular, it involves collecting information on actual or anticipated customer needs and feeding this into the product development process, while simultaneously preparing the marketplace and marketing for the new product. It is essential throughout this process that a dialog is maintained with other functions involved in the development process and that the process of development is staged via a series of “gates,” which control progress and resource commitment.

  A key aspect of the marketing effort involves anticipating likely responses to new product concepts and using this information to design the product and the way in which it is launched and marketed. This process of analysis builds upon knowledge about various sources of what Thomas calls “market friction” [119].

  Recent years have seen a considerable surge in interest around “agile innovation,” a term used to describe a series of methods that originated in the field of software development [120]. It has been increasingly applied to other development projects for new products, services, and even process reengineering. At its heart is an approach that emphasizes focused high-intensity team work (often called a “scrum”), stretching goals, and rapid cycles of prototyping, testing, and learning. Where conventional project management techniques set a goal and then break down the various tasks needed to complete it into key activities and allocate resources to them, agile methods are more open-ended, allowing considerable creativity and flexibility in the execution of activities, which will move nearer to the stretch target.

  Lean start-up (LSU) is a similar approach for entrepreneurs developed by Eric Ries and popularized by him and Steve Blank in various books and articles [121,122]. It draws on his own experience as an entrepreneur and his reflections on what went wrong with the process. At its heart, with agile innovation, is the view that starting a new venture is about a series of short fast experiments rather than a carefully planned and executed big project. Each cycle is carefully designed to generate information and test ideas out on the market – and after each prototype, the venture idea is adjusted. Key principles are the “minimum viable product” (MVP), which is a simple basic version of the overall product idea, which can be tested on users to gain feedback, and the “pivot,” which changes in direction as a result of that feedback.

  We discuss lean and agile methods in more detail in Chapter 10.

• Launching and sustaining innovation of new products, services, or processes brings the need to understand the dynamics of adoption and diffusion. Buyer behavior is a complex subject, but there are several key guidelines that emerge to help shape market development for a new product. The first is the underlying process of adoption of something new; typically, this involves a sequence of awareness, interest, trial, evaluation, and adoption. Thus, simply making people aware, via advertising, and so on, of the existence of a new product will not be sufficient; they need to be drawn into the process through the other stages. Converting awareness to interest, for example, means forging a link between the new product concept and a personal need (whether real or induced via advertising). Chapter 9 deals with this issue in greater depth.

  Successful implementation of internal (process) innovations also requires skilled change management. This is effectively a variation on the marketing principles outlined earlier and stresses communication, involvement, and intervention (via training, etc.) to minimize resistance to change – again essentially analogous to Thomas’s concept of “market friction.” Chapter 9 discusses this theme in greater detail and presents some key enabling routines for the implementation phase.

  Understanding user needs has always been a critical determinant of innovation success, and one way of achieving this is by bringing users into the loop at a much earlier stage. The work of Eric von Hippel and others has shown repeatedly that early involvement and allowing them to play an active role in the innovation process leads to better adoption and higher quality innovation. It is, effectively, the analog of the early involvement/parallel working model mentioned earlier – and with an increasingly powerful set of tools for simulation and exploration of alternative options, there is growing scope for such an approach [123,124].

  Where there is a high degree of uncertainty – as is the case with discontinuous innovation conditions – there is a particular need for adaptive strategies, which stress the coevolution of innovation with users, based on a series of “probe and learn” experimental approaches. The role here for early and active user involvement is critical.

Capturing Value

The purpose of innovating is rarely to create innovations for their own sake, but rather to capture some kind of value from them – be it commercial success, market share, cost reduction or – as in social innovation – changing the world. History abounds with examples of innovations that succeeded at a technical level but that failed to deliver value – or achieved it briefly, only to have the advantage competed away by imitators. Capturing value from the process is a critical theme and one to which we will return in Chapter 11. There are many ways in which this can be done, from formal methods, such as patenting through to much less formal, such as the use of tacit knowledge. And central to the discussion is the concept of “complementary assets” – what other elements around the system in which the innovation is created and delivered are hard for others to access or duplicate? This gives rise to the idea of what David Teece [83] termed “appropriability regimes” – how easy or hard is it to extract value from investments in innovation?

An inevitable outcome of the launch of an innovation is the creation of new stimuli for restarting the cycle. If the product/service offering or process change fails, this offers valuable information about what to change for the next time. A more common scenario is what Rothwell and Gardiner call “reinnovation”; essentially building upon early success but improving the next generation with revised and refined features. In some cases, where the underlying design is sufficiently “robust,” it becomes possible to stretch and reinnovate over many years and models [125].

But although the opportunities emerge for learning and development of innovations and the capability to manage the process that created them, they are not always taken up by organizations. Among the main requirements in this stage is the willingness to learn from completed projects. Projects are often reviewed and audited, but these reviews may often take the form of an exercise in “blame accounting” and in trying to cover up mistakes and problems. The real need is to capture all the hard-won lessons, from both success and failure, and feed these through to the next generation. Nonaka and Kenney provide a powerful argument for this perspective in their comparison of product innovation at Apple and at Canon [126]. Much of the current discussion around the theme of knowledge management represents growing concern about the lack of such “carryover” learning – with the result that organizations are often “reinventing the wheel” or repeating previous mistakes.

Learning can be in terms of technological lessons learned – for example, the acquisition of new processing or product features – which add to the organization’s technological competence. But learning can also be around the capabilities and routines needed for effective product innovation management. In this connection, some kind of structured audit framework or checklist is useful.

2.9 Beyond the Steady State

The model we have been developing in this chapter is very much about the world of repeated, continuous innovation where there is the underlying assumption that we are “doing what we do but better.” This is not necessarily only about incremental innovation – it is possible to have significant step changes in product/service offering, process, and so on – but these still take place within an established envelope. The “rules of the game” in terms of technological possibilities, market demands, competitor behavior, political context, and so on, are fairly clear, and although there is scope for pushing at the edges, the space within which innovation happens is well defined.

Central to this model is the idea of learning through trial and error to build effective routines, which can help improve the chances of successful innovation. Because we get a lot of practice at such innovation, it becomes possible to talk about a “good” (if not “best”) practice model for innovation management, which can be used to audit and guide organizational development.

But we need to also take into account that innovation is sometimes discontinuous in nature. Things happen – as we saw in Chapter 1 – which lie outside the “normal” frame and result in changes to the “rules of the game.”Under these conditions, doing more of the same “good practice” routines may not be enough and may even be inappropriate when dealing with the new challenges. Instead, we need a different set of routines – not to use instead of but as well as those we have developed for “steady-state” conditions. It is likely to be harder to identify and learn these, in part because we don’t get so much practice – it is hard to make a routine out of something that happens only occasionally. But we can observe some of the basic elements of the complementary routines, which are associated with successful innovation management under discontinuous conditions. These tend to be associated with highly flexible behavior involving agility, tolerance for ambiguity and uncertainty, emphasis on fast learning through quick failure, and so on – very much characteristics that are often found in small entrepreneurial firms.

As we will see throughout the book, a key challenge in managing innovation is the ability to create ways of dealing with both sets of challenges – and if possible to do so in “ambidextrous” fashion, maintaining close links between the two rather than spinning off completely separate ventures.

Summary

In this chapter, we’ve looked at the challenge of managing innovation as a core business process concerned with renewing what the organization offers and the ways in which it creates and delivers that offering. The process has a number of elements, and we will explore these in more detail in the rest of the book. We have also looked at the question of routines – repeated and learned patterns of behavior, which become “the way we do things around here” since it is these that constitute the core of innovation management capability. Finally, we looked at some of the lessons learned around success routines – what does experience teach us about how to organize and manage innovation?

A number of writers have looked at innovation from a process perspective; good examples include Keith Goffin and Rick Mitchell’s “Innovation management” (Palgrave, London, 2010), Paul Trott’s “Innovation and new product development” (Pearson, London, 2011), and Andrew Van de Ven’s “Innovation journey” (Oxford University Press, 1999). Case studies provide a good lens through which this process can be seen, and there are several useful collections including Bettina von Stamm’s “Innovation, design and creativity” (2nd edition, John Wiley, 2008), Roland Kaye and David Hawkridge’s “Case studies of innovation”(Kogan Page, London, 2003), and Roger Miller and Marcel Côté’s “Innovation reinvented: Six games that drive growth” (University of Toronto Press, 2012). For practitioners, Gijs van Wulfen’s books “The innovation expedition” (BIS, 2013) and “The innovation maze” (BIS, 2016) take metaphors around traveling through the process to help understand key issues and potential management action.

Some books cover company histories in detail and give an insight into the particular ways in which firms develop their own bundles of routines - for example, David Vise’s “The Google story” (Pan, London, 2008), Graham and Shuldiner’s “Corning and the craft of innovation” (2001, Oxford University Press), and Gundling’s “The 3M way to innovation: Balancing people and profit” (2000, New York: Kodansha International).

Autobiographies and biographies of key innovation leaders provide a similar – if sometimes personally biased – insight into this. For example, Richard Brandt’s “One click: Jeff Bezos and the rise of Amazon.com,” (Viking New York, 2011), Walter Issacson’s “Steve Jobs: The authorised biography” (Little Brown, New York, 2011), and James Dyson’s “Against the odds” (Texere, London, 2003). In addition, several websites – such as the Product Development Management Association (www.pdma.org), Innovation Excellence (http://innovationexcellence.com/), and http://www.innovationmanagement.se/ – carry case studies on a regular basis.

Many books and articles focus on particular aspects of the process – for example, on technology strategy, Burgelman et al.’s “Strategic management of technology” (McGraw-Hill Irwin, 2004). On product or service development, Robert Cooper’s “Winning at new products” (Kogan Page, 2011), Rosenau et al.’s “The PDMA Handbook of new product development” (John Wiley, 2013, 3^rd edition), and Tidd and Hull’s “Service innovation: Organizational responses to technological opportunities and market imperatives” (Imperial College Press, 2003). On process innovation, Lager’s “Managing process innovation” (Imperial College Press, 2011), Zairi and Duggan’s “Best practice process innovation management” (Butterworth Heinemann, Oxford, 2012), and Gary Pisano’s “The Development factory: Unlocking the potential of process innovation,” (Harvard Business School Press 1996). On technology transfer, Mohammed Saad’s “Development through technology transfer,” Intellect, 2000). On implementation, Alan Afuah’s “Innovation Management: Strategies, Implementation and Profits” (Oxford University Press, 2003), Osborne and Brown’s “Managing change and innovation in public service organizations” (Psychology Press, 2010), and Bason’s “Managing public sector innovation” (Policy Press, London, 2011). On learning, Kim and Nelson’s “Technology, learning, and innovation: Experiences of newly industrializing countries” (Cambridge University Press, 2003), Nooteboom’s “Learning and innovation in organizations and economies” (Oxford University Press, 2000), Leonard’s “Wellsprings of knowledge” (Harvard Business School Press, 1995), and Nonaka’s “The knowledge creating company” (Harvard Business School Press, 1991).

Websites such as NESTA (https://www.nesta.org.uk/) regularly report academic research around innovation, and blogsites such as http://innovationexcellence.com/ and http://www.innovationmanagement.se/ offer useful practical tools and perspectives.

Case Studies

You can find a number of additional downloadable case studies at the companion website including:

• a case study of Tesco and their (failed) innovation based on market entry to the United States, which gives an insight into how large retailers approach innovation
• case studies from the public sector – RED and Open Door – and from the humanitarian sector, which give some insight into how innovation is approached in not-for-profit contexts
• a case study of Zara showing how IT and networks support fast fashion as an innovation model
• several cases – AMP, Law Firms, MPESA, and NPI – which illustrate innovation in financial and legal sectors
• case examples – Threadless, Adidas, Joseph’s, Lego – where companies are exploring user-led approaches
• case study of Liberty Global, which describes their efforts to create and sustain a culture of continuous incremental innovation
• case studies of Aravind, NHL Hospitals, Lifespring Hospitals, and Eastville Community Shop as examples of social innovation
• case studies of Cerulean, Coloplast, and Philips, which explore the issues in creating and executing radically new projects within a large organization
• case histories of Marshalls and Hella, which show how innovation develops over an extended period of time within organizations
• an audio interview with Lynne Maher describing innovation in the UK health system