Technology, Strategy and Organisation (Day 2)

Product	Innovator	Follower	The winner
Jet airliner	De Haviland (Comet)	Boeing (707)	The winner
Float glass	Pilkington	Corning	Leader
X-ray scanner	EMI	General Electric	Follower
Office PC	Xerox	IBM	Follower
VCRs	Ampex/Sony	Matsushita	Follower
Diet Cola	R.C. Cola	Coca-cola	Follower
Instant camera	Polaroid	Kodak	Leader
Pocket calculator	Bowmar	Texas instruments	Follower
Microwave oven	Raytheon	Samsung	Follower
Plain-paper copier	Xerox	Canon	Not clear
Fibre-optic cable	Corning	Many companies	Leader
Videogames consoles	Atari	Sony/Nintendo/Microsoft	Followers
Disposable diaper	Proctor and Gamble	Kimberley-Clark	Leader
Web browser	Netscape	Microsoft	Follower
MP3 players	Diamond multimedia	Apple/Sony/others	Followers

Paradoxical nature of strategy

“At the heart of every set of strategic issues, a fundamental tension between apparent opposites can be identified… Each pair of opposites… seem to be inconsistent, or even in compatible, with one another… If firms are competing, they are cooperating. If firms must comply to the industry context, they have no choice. Yet, although these opposites confront strategists with conflicting pressures, strategists must somehow deal with them simultaneously.” (De Wit and Meyer, 2004:13)

Different types of tension in organizational decision-making

-       Puzzle = challenging problem with an optimal solution.

-       Dilemma = vexing problem with two possible solutions, neither of which is logically the best. They confront problem solves with diffulcut either-or choices.

-       Trade-off = problem situation in which there are many possible solutions, each striking a blance between conflicting pressures

-       Paradox = a situation in which two seeming contradictory, or even mutually exclusive, factors appear to be true at the same time. It is a problem with no real solution, or one which is internally consistent. Thus paradox can be characterized as a ‘both-and’ rather than ‘either-or’ problem, where e the solution requires the accommodation of opposites.

Paradoxes in the management of innovation

The following are in constant tension:

-       Planned versus Emergent (strategy & projects)

-       Top-down versus bottom-up (strategy)

-       Formal versus informal (structures & processes)

-       Exploration verssu exploitation of ideas

-       Radical versus incremental innovation

-       Internal (in-house versus external (outsourcing)

Managing multiple innovation strategies

Organizations may follow:

-       Different strategies at different times

-       Different strategies in different sectors

-       Different strategies in different divisions

Key questions

-       What patterns exist in the life of a technology and/or innovation?

-       What models exist to help explain and map such patterns?

-       To what extent are such models useful?

-       What are the implications of strategy-making

Models to be discussed

1. The ‘Technology Life Cycle’ (TLC) model – this maps the sales volume trajectory of a technology over time.
2. The ‘Technology S-curve’ model – this maps the technical performance trajectory of a technology in relation to research and development (R&D) effort.
3. The ‘Product-Process Cycle’ model – this maps the interrelated trajectories over time of product and process innovations within a sector.
4. The ‘Dominant Design’ model – this maps the emergence of a dominant design of an innovation or technology over time.
5. The ‘Diffusion Curve’ model – this maps the diffusion trajectory of an innovation ortechnology over time.

Features of models

-       All of the models are dynamic in nature;

-       Each can be grouped according to whether it attempts to map the ‘market’ performance trajectory or the technical performance trajectory of a technology;

-       Each typically adopts either a cumulative or a non-cummulative approach to expressing and mapping progress

-       Each model may be classified in relation to the four ‘motors of change’

The ‘Technology Life Cycle’ (TLC) Model

Mapping the sales volume trajectory of a technology over time

The ‘Technology S-Curve’ model

Mapping the technical performance trajectory of a technology in relation to research and development (R&D) effort.

Initial effort doesn’t provide performance returns due to the ground work, back ground knowledge and research. Performance is measured against a metric related to the product rather than knowledge accumulated.

Exponential growth

S-Curve Model Examples

S-Curve for the automobile	Series of shifting S-Curves of Semiconductor Technology
S-curve for Intel microprocessor speed	S-curve for Intel microprocessor density

Over view of the S-Curve model

Key Features	The model highlights the changing relationship between R&D effort (inputs) and improvements in the technical performance of a technology (outputs) over its lifecycle. The ‘technical limit’ and the technical potential (i.e. the gap between the current position of the curve of an organization or the state-of the-art and the technical limit) are key elements of the model.
Implications for managing innovation	Early on the life of a technology, patience is required, because R&D effort yields are low as the organization and sector build foundational knowledge and skills. The diminishing returns on R&D are important for signaling the narrowing of technical potential and the approach of the technical limit, and thus are key for highlighting the need to look for alternative technologies
Critique-limitations	It represents a fairly crude input-output model, which black boxes’ important aspects of process and context. It is also very difficult to determine or measure with any accuracy they key elements of the model, such as the technical limit or the current position on the curve of the organization or state-of-the-art
Critique-complications in application	The important performance criteria for a technology are subject to change and are often in tension with one another. A technology may be compromise of

The ‘Product-Process Cycle’ Model

Mapping the interrelated trajectories over time of product and process innovations within a sector

Innovation characteristics linked to phases of the product-process cycle

Innovation characteristic	Fluid pattern	Transitional phase	Specific phase
Competitive emphasis placed on…	Functional product performance	Product variation	Cost reduction
Innovation stimulated by…	Information on user needs, technical inputs	Opportunities created by expanding internal technical capability	Pressure to reduce cost, improve quality, etc.
Predominant type of innovation	Frequent major changes in products	Major process innovations required by rising volume	Incremental product and process innovation
Product line	Diverse, often including custom designs	Includes at least one stable or dominant design	Mostly undifferentiated standard products
Production processes	Flexible and inefficient – aim is to experiment and make frequent changes	Becoming more rigid and defined	Efficient, often capital intensive and relatively rigid

The reverse product-process cycle

An overview of the product-process cycle model

Key Features	The product-process cycle incorporates three phases, each with particular patterns of innovation, competition, industry structure, and organization The ‘fluid’ phases; The ‘transitional’ phase; and The ‘specific’ phase. During the life cycle, there is a shift from product to process innovation, major improvement to incremental innovation, entrepreneurial to large ‘mechanistic’ organizations, and competition based on differentiation to that based on cost.
Implications for managing innovation	At the organizational level, each phase of the model requires a different strategic orientation, centred on the balance between innovation and efficiency; and a different mode of organizing, concerning the balance between the ‘organic’ and mechanistic’ modes as well, as different competences and resources. At the sectoral level, different market conditions and competitive pressures characterize each phase of the model.
Critique-limitations	The original model is ‘unidirection’ and ‘irreversible’, however, subsequent versions allow for de-maturity during the ‘life course’ rather than ‘life cycle’ of a technology. The model has little efficacy when applied to a service sector, hence the development of reverse product-process cycle. It underemphasizes problems of transition for incumbent organizations
Critique-complications in application	Identifying the revolutionary transition between phases of the model is problematic, in part due to the ‘fuzziness’ of their boundaries. It is difficult to apply to complex products, such as computer, automobiles, and aircraft, which are comprised of nested levels of technology.

The ‘Dominant Design’ Model

Mapping the emergence of a dominant design of an innovation or technology over time.

Stretching the dominant design through incremental innovation

Incremental innovation in:

-       Handlebar arrangement

-       Saddle position

-       Huge gear

-       Short wheelbase

-       Riding position

… all lead to a very aerodynamic bicycle

Factors influencing the emergence of a dominant design

-       Relative marketing and advertising expenditure

-       Relative scope and scale of the distribution channels

-       Relative brand (corporate and product) strength

-       Degree of standard setting – influenced by regulation and/or industry cooperation

Shaping industry standards – the case of HDDVD and Blu-Ray

<insert notes>

Management implications of the emergence of a dominant design

-       Diffusion mechanisms and standards setting play an important part in the emergence of a dominant design;

-       Late entrants to the market are unlikely to be able to comete directly with the dominant design;

-       Innovation opportunities exist in stretching the dominant design (the improvement of components, accessories and material) or developing niches.

An overview of the dominant design model

Key features	In the early phase of a new technology, a range of divergent designs are present. After a time, however, there is a ‘shakeout’ as the market converges around one or a small number of designs that eventually dominate. Opportunities remain in relation to ‘stretching’ the design through innovation in materials and components, or in the development of new design families
Implications for managing innovation	The emergence of a dominant design or industry standard in a market signals the ending of a period of technological turbulence and competitive ferment. With the emergence of a dominant design comes the narrowing of opportunities for new entrant, and the need for incumbent organizations to influence the choice of design in the market and/or the industry standard.
Critique – limitations	The model is ‘unidirectional’, despite the recognition of niche opportunities. The model itself says little about why or how dominant designs emerge. But subsequent research has highlighted non-technological factors, such as developing loyalty among the user base, building wide distribution networks, and influencing standard-setting processes.
Critique – complications in application	It is time-consuming to apply to complex products, such as computer, automobiles, and aircraft, which are comprised of nested levels of technology; dominant designs may exist within each of these levels, and emerge ad different times and rates.

The ‘Diffusion Curve’ Model

Mapping the diffusion trajectory of an innovation or technology over time.

Foster 1986

The innovativeness dimension, as measured by the time at which an individual adopts and innovation or innovations, is continuous. The innovativeness variable is partitioned into five adopted categories by laying off standard deviations from the average time of adoption (x). The model is useful for reporting, but not for forecasting.

• Innovators – Recognise the early market needs
• Early Adopters – Centre of communication ‘Free advertising’
• Early Majority – Momentum building
• Late Majority – Followers
• Laggards – Don’t care, objectors

Factors influencing diffusion innovation characteristics

-       Relative advantage – the degree to which an innovation is perceived by potential adopters to be ‘better’ than alternatives;

-       Compatibility – the degree to which an innovation is perceived by potential adopters to be aligned or consistent with their prevailing needs, values or experience

-       Complexity – the degree to which an innovation is perceived by potential adopters to be difficult to understand or use;

-       Trialability – the degree to which an innovation can be experimented with by potential adopters prior to adoption;

-       Observability – the degree to which the benefits of an innovation can be observed by potential adopters

EXAMPLE: Diffusions and the case of virtual reality

Limitations

• No accounting for external factors (social, political, fuel prices, regulators)
• No interdependence
• Reporting – not forecasting
• Performance improvements / Functionality increase

Transitions between

Innovators -> Early Adopters

• Relevant marketing / advertising
• Distribution Channels
• Policy and Standards
• Brand Strength
• Social Networks

Different shapes for the product life cycle

An overview of the diffusion curve model

Key features	The diffusion curve represents the typical diffusion pattern for an innovation. The model identifies five adopter categories, as well as five innovation attributes, which help to identify features of both users and innovations that impact the speed of diffusion/adoption of an innovation.
Implications for managing innovation	Different adopter categories have different roles to play in the diffusion adoption process, as well as in the innovation process. Shaping the innovation with regard to the five innovation attributes identified will help promote the rate of diffusion/adoption of an innovation.
Critique-Limitations	Without reliable market data for the potential groups that are likely to adopt the innovation, and thus the overall potential number of adopters, the diffusion curve model essentially becomes relegated to a reporting tool rather than a forecasting tool. The model does not explicitly take account of subsequent major improvements in the performance, cost, or functionality of the innovation that might alter the pattern of its diffusion.
Critique – complications in application	The diffusion pattern of ‘interactive’ innovations, such as the telephone, fax, email, and teleconferencing, differs from the diffusion pattern of discrete or non-interactive innovations. An innovation may be adopted, rejected, adopted and adapted, or adopted and then later abandoned.

An overview of the key features of the models

Model	Introduction stage	Growth stage	Maturity stage
Technology S-Curve	The rate of improvement in the technical performance of a technology is slow, because much of the R&D effort is required to develop the basic knowledge underpinning the technology. As a result, the technical performance returns from R&D effort are low.	As knowledge about a technology accumulates and is diffused and applied, the rate of progress in the improvement of a technology begins to accelerate. Here, the technical performance returns from R&D effort are high. But after the ‘point of inflection’ (the middle of the S-Curve) at which the yield is at its highest, the innovator begins to suffer from decreasing returns.	The yield on R&D effort begins to decline at an increasing rate as it approaches the ‘natural’ or ‘technical’ limits of the technology.
Product-process cycle	Associated with the emergence of a new market need or a new way of meeting an existing market need. Initially, in meeting this need, there is often vagueness concerning the appropriate performance criteria of the new product offering. As a result this phase is characterized by frequent major changes to products as well as diversity of products in the market place.	In order to meet rising demand for the new product, there is a shift away from frequent major product innovation to major process innovation. This phase is associated with emergence of one or a small number of stable product designs to allow significant production volumes, and the development of ‘islands of automation’.	There is a focus on cost reduction and product quality, and a shift towards incremental product and process innovation to bring about such improvements. The gradual and cumulative impact of ‘countless’ minor incremental product and process innovations can have a dramatic impact on productivity.
Dominant Design	The emergence of a new technology leads to a great variety of competing innovations with divergent designs in delivering the technology to the market place	There is a shakeout among the divergent set of design initially available in the marketplace, followed by the evolutionary improvement of a narrow range of ‘composite’ designs.	As the market matures, an even narrower set of consolidated designs emerges, with the possibility of the emergence of a single dominant design. Once establish in the market place the dominant design can then be ‘stretched’, through the development of new materials, components, and accessories, or through the development of the design families
Diffusion curve	‘Innovators’, with a keen interest in new idea, are the first to adopt and innovation. They are a small minority of the overall group 9i.e. only about 2.5%). Early adopters’, unlike innovators are embedded in their social system, they are ‘localities’ rather than ‘cosmopolites’. This group represents a large proportion of the overall group, although still a minority (i.e. around 13.5%)	The ‘early majority’ are individuals who adopt an innovation just before the average person. They make up about one third of the overall group. Although they take long to deliberate before adopting or rejecting an innovation than innovation and early adopters, they have an important role to play in the diffusion process, through creating momentum in the diffusion of the innovation and social pressure on non-adopters to adopt, and help to build a critical mass	The ‘late majority’ are individuals who adopt innovations after the average person. They also make up about one third of the overall group. This group tends to be cautious or skeptical of new ideas. Often, peer pressure is an important stimulus for this group to adopt. ‘Laggards’ are the last to adopt. They constitute about one sixth of the overall group. They are generally suspicious of new ideas and change, and their point of reference tends to be the past.

Conclusions

-       Such models allow managers to view their industry from an historical and temporal perspective;

-       They provide the language and tools for discussing the past, current, and future technological environment; an important prerequisite to the formulation of strategy

-       However, such models are overly deterministic, and underplay the potential for organizations to influence the shape of, and speed of movement along, a trajectory;

-       There is an over emphasis on the ‘supply-side’.

Extra reading: Managing and shaping innovation – the patterns of innovation within the life cycle of a technology

Technology, Strategy and Organisation (Day 1)

Here is the audio from the first day of the TSO module

Session 1-4

A discussion on selected case studies; Canned Laughter Machine. Examining the service-product element in modern servitization.

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Session 1-5

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What is strategy?

Quinn states that “a strategy is the pattern or plan that integrates and organisations goals, policies and action sequences into a cohesive whole.”

Chandler stated that “strategic decisions are concerned with the long-term health of the enterprise”.

Porter noted that “Strategy is about setting yourself apart from the competition” This is echoed by Ohmae whostates that “Business strategy is all about competitive advantage”.

Elements of strategic management

Some have stated that there are three aspects to strategic management which include;

1. Strategic analysis
2. strategic choice
3. Strategy implementation.

It should be noted that there are strong relationships between all three elements.

Is strategy different from management

Strategy can sometimes be confused with management. The differences are in 3 broad areas which are related to:

1. Timescale. Management is often looking over a short time frame, typically between 1-12 months, whereas strategy is longer term focused looking at time frames typically between 1-10 years.
2. Level of analysis. Management is concerned with a lower level of a business, the micro level, where as strategy is looking at the Macro level workings of the organisation.
3. The level of abstraction. Strategy is concerned with the conceptual more philosophical aspects i.e. “should we develop strategic technology alliances with suppliers?” where as management is looking at more concrete aspects such as “how do we improve our staff scheduling procedures?”

Types of decision

There are two types of decision in relation to management and strategy, which include:

1. Programmed decisions which are, routing, undertaken frequently and often operational;
2. And non-programmed decisions which are non-routine, undertaken infrequently or one-off decisions; often at strategy or policy level, greater potential for goal & means uncertainty.

Generally across organisations, non-programmed decisions are made by top management, where as programmed decisions are the responsibility of people lower down the ladder.

Corporate strategy

So corporate level strategy is concerned with:-

-       Overall purpose and scope of the organisation

-       Adding value to shareholder investment

-       Portfolio issues

-       Resources allocation between Strategic Business Unit (SBUs)

-       Structure and control of SBUs

-       Corporate financial strategy

Business strategy

Strategic Business Unit (SBU) level strategies are concerned with:-

-       Competitive strategy

-       Developing market opportunities

-       Developing new products/services

-       Resource allocation within the SBU

-       Structure and control of the SBU

Operational strategy

Operational strategy is concerned with:

-       The integration of resources, processes, people and skills

-       The implementation and realisation of corporate business strategy

Mintzberg –the strategy concept:

-       Strategy as a plan – a consciously intended course of action

-       Strategy as a pattern – a stream of actions

-       Strategy as a position – location the organisation within an environment

-       Strategy as perspective – as a way of perceiving the world

4Ps (again) Plan, Pattern, Position, Perspective

Explanation of strategy formulation

There are several explanations of strategy formulation, strategy as:

-       Managerial intent

-       Outcome of cultural and political processes,

-       externally imposed

Rational decision making process

The notes also outline a rational decision making process that goes:

1. identify problem
2. determine alternative courses fo action
3. choice criteria and select optimal course of action
4. implement
5. monitor and evaluate

Alternative models of decision making

There is also discussion of alternative models of decision making:

-       “Bounded rationality”

-       “Satificing “

-       “Logical incrementalism”

-       “Muddling Through”

-       Political

-       Cultural

Strategy formulation: Planned versus emergent

“Strategy deals with the unknowable, not the uncertain… hence logic dictates that one proceed flexibly and experimentally from broad concepts toward specific commitments, making the latter concrete as late as possible in order to narrow the bands of uncertainty and to benefit from the best available information. This is a process of ‘logical incrementalism’… [it] is not ‘muddling’… it is conscious, purposeful, proactive, good management.” (Quinn 1988b:104)

Strategy Formulation

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Strategy Development

Strategy Formulation (Burgelman and Sayles, 1986: 13)

“Understandably we think of strategy formulation as top management work… But in the high-technology world, strategy often revolves around the innovation activities of relatively low-level technical and business people. To be sure, their decisions will require ratification by top management. Nevertheless… the reality is that those closer to the emerging technology will seek to define the business opportunity”

The constraints to strategic choice

-       Distinction between ‘environmental and ;action determinism’ Whittington (1998)

-       ‘CoreRigidities’ – core competences can become core-rigidities’ if they dominate and shape management vision and the development of organisational skills (Leonard-Barton 1995)

-       ‘Zones of Manoeuvre’ – “Contexts contain constraints as well as zones of manoeuvre” (Clark 2000:297)

Challenges of strategic management

-       The need to manage complexity and ambiguity

-       The need to conceptualise options and issues

-       The requirement to reconcile the influences of a changing environment, stakeholder expectations and resource capabilities

-       To need to identify or create strategic opportunities

-       And manage change

Competing paradigms

-       Structure – Conduct – Performance Paradigm: Strategy is shaped by the structure of the industry in which the company operates i.e. Environment-Led

-       Resource Based Value Paradigm – Strategy is shaped bya focus on company-specific assets that provide a company with competitive advantage i.e. Resource-Led.

Comparing Paradigms

Aspect of Strategy	Environmental-Led ‘Fit’	Resource-Led ‘Stretch’
Underlying basis of strategy	Strategic fit between market opportunities and organisation’s resources	Leverage of resources to improve value for money
Competitive advantage through	“Correct” positioning. Differentiation directed by market need	Differentiation based on competences suited to or creating market need
How small players survive	Find and defend a nich	Change the ‘rules of the game’
Risk Reduction through…	Portfolio of products/businesses	Portfolio of competences
Corporate centre invests in …	Strategies of divisions or subsidiaries	Core competences

Porters Five Forces

1. Rivalry among competitors
2. Potential entrants (threat of new entrants)
3. Substitutes (threat of substitute products/services)
4. Suppliers (bargaining power of suppliers)
5. Buyers (bargaining power of buyers)

Competitive rivalry is high when:

-       Entry is likely

-       Substitutes threaten

-       Buyers or suppliers exercise control

-       Competitors are in balance

-       There is slow market growth

-       Global customers increase competition

-       There are high fixed costs in an industry

-       Markets are undifferentiated

-       There are high exit barriers

Threat of entry is dependent on barriers to entry such as:

-       Economies of scale

-       Capital requirements of entry

-       Access to distribution channels

-       Cost advantages independent of size (e.g. the experience curve)

-       Expected retaliation

-       Legislation or government action

-       Differentiation

Supplier power is high when:

-       There is a concentration of suppliers

-       Switching costs are lower

-       The supplier brand is powerful

-       Integration forward by the supplier is possible

-       Customers are fragmented and bargaining power low

Buyer power is likely to be high when:

-       There is a concentration of buyers

-       There are many small operators in the supplying industry

-       There are alternative sources of supply

-       Components or materials are a high percentage of cost to the buyer leading to “shopping around”

-       Switching costs are low

-       There is a threat of backward integration

Threat of substitutes – substitutes take different forms:

-       Product substitution

-       Substitution of need

-       Generic substitution

-       Doing without

Portfolio Analysis: McKinsey GE Matrix (Market Attractiveness vs. Business Strength)

Factors that Affect Market Attractiveness

Whilst any assessment of market attractiveness is necessarily subjective, there are several factors which can help determine attractiveness. These are listed below:

-       Market Size

-       Market growth

-       Market profitability

-       Pricing trends

-       Competitive intensity / rivalry

-       Overall risk of returns in the industry

-       Opportunity to differentiate products and services

-       Segmentation

-       Distribution structure (e.g. retail, direct, wholesale

Factors that Affect Competitive Strength

Factors to consider include:

-       Strength of assets and competencies

-       Relative brand strength

-       Market share

-       Customer loyalty

-       Relative cost position (cost structure compared with competitors)

-       Distribution strength

-       Record of technological or other innovation

-       Access to financial and other investment resources

In summary technology strategy is…

-       The total pattern of decisions

-       That shape the capabilities which determine

-       The contribution of any type of technology

-       To overall strategy

Technology, Strategy and Organisation (Day 3)

Key questions

-       What is a technological discontinuity and how might it be explained?

-       What impact does technological discontinuity have on organizations, sectors, and regions?

-       How might organizations survive such discontinuities?

-       Are there links between economic and technological cycles?

Continuity vs. Discontinuity

Continuity	Discontinuity
Focus on innovation tied to an existing technology and related to its movement along an existing technological trajectory. Focuses on a single cycle of technology Is characterized by stability Involves knowledge accumulation Is competence enhancing/strengthening Involves incremental innovation	Focus on innovation that gives rise to a new technology and a shift to a new technological trajectory Focuses on multiple cycles of technology Is characterized by instability Involves ‘creative destruction’ Is often competence destroying/disrupting Involves radical innovation

Thomas Kuhn and continuity

Scientific Paradigm is a set of accepted scientific laws, theories, and practices – around which scientists coalesce. They provide the guidelines for research, unfluencing the ‘puzzles’ that are considered to be worthy of pursuit and those that are not. This process leads to the limitation of novelty and a focus on ‘the scope and precision with which the paradigm can be applied’.

‘Normal Science’ is actualization of paradigm achieved by extending the knowledge of those facts that increasing the extent of match between those facts and the paradigm’s predictions, and by further articulation of the paradigm itself.

Technological paradigms / regimes

Technological paradigm is the beliefs of engineers, in relation to ‘what is [considered ] feasible or at least worth attempting’ (Nelson & Winter 1977) and an ‘outlook’ and set of procedures which embody ‘strong prescriptions on the directions of technical change to pursue and those neglect’ (Dosi 1982),

Concept	Proponent(S)	Definition	Orientation
Technological imperatives	Rosenberg (1969)	R&D is directed toward the imperfections of existing technology, such as bottlenecks in processes or weaknesses in products, which provide signals for prioritizing the R&D projects. Pursuing such ‘technological imperatives’ pushes a technology in a particular direction.	Inducements and signals
Technological regime	Malerba and Orseningo (1993)	“We defined technological regimes as combinations of opportunity and appropriability conditions and degrees of cumulativeness of technological advances… Opportunity conditions refer to the ease of innovation by would-be innovators, and are related to the potential for innovation of each technology. Appropriability conditions refer to the ability of innovators to protect their innovations from imitation, and therefore to reap profits from their innovations. Cumulativeness conditions refer to the degree to which new technology builds on existing technology.’’	Inducements and signals
Technological paradigm	Dosi (1982)	“We shall define ‘technological paradigm’… as an ‘outlook’, a set of procedures, a definition of the ‘relevant’ problems and of the specific knowledge related to their solution… each ‘technological paradigm’ defines its own concept of ‘progress’ based on its specific technological and economic trade-off… [and as such] embodied strong prescriptions on the directions of technical change to pursue and those to neglect.’	Cognitive
Technological regime	Nelson and Winter (1977)	“Our Concept is more cognitive, relating to technicians’ beliefs about what is feasible or at least worth attempting… the sense of potential of constraints, and of not yet exploited opportunities, implicit in a regime focuses attention of engineers on certain direction in which progress is possible, and provides strong guidance as the tactics likely to be fruitful.’	Cognitive
Technological regime	Rip and Kemp (1998) Kemp et al. (1998), Ende and Kemp (1999)	‘Technological regimes, in the way we use the term, are a broader, socially embedded version of technological paradigms,.’ (Kemp et al. 1998: 182). The notion of regime helps to focus the attention on the structure of which the actors and technologies are a part of… on rules and practices, embedded in a web of interrelations and ongoing trends as a backdrop… we are not saying… either individuals and companies are unimportant… [but they]… are equipped with a certain outlook, capabilities and role, which influences both what they will do and can do at any given time… the research activities of companies are shaped importantly by the problems of existing regimes and the accumulated knowledge, capital stock, established consumption patterns and the norms at the macro level	Socially embedded cognition
Socio-technical regime	Geels (2002)	‘While the cognitive routines of Nelson and Winter are embedded in the practices and minds of engineers, these rules are [also] embedded more widely in the knowledge base, engineering practices, corporate governance structures, manufacturing processes and product characteristics. This widening also means that more social groups are taken on board… users, policy makers, societal groups, suppliers, scientists, capital banks etc. because the activities of these groups are also guided by rules, I will use the term ‘sociotechnical [ST] regimes’ to refer to the semi-coherent set of rules carried by different social groups… ST regimes thus function as {a} selection and retention mechanism.’	Socially embedded cognition
Technological frame	Orlikowski and Gash (1994) Kaplan and Tripas (2008)	“A ‘technological frame’… captures how actors make sense of technology… specifically, technological frames shape how actors categorize a technology relative to other technologies which performance criteria they use to evaluate the technology… [it] guides the actor’s interpretation of what a technology is and whether it does anything useful… [they] do not spring up randomly, but rather are the encoding of… [an actor’s] prior history, including both idiosyncratic organizational experiences and industry affiliations… with industry associations, customer sets, competitive groups, user groups etc… Technological frames do not influence technologies directly but rather through the interpretive processes of these actors. Thus the interpretative process is the mechanism that connects technological frames to technological outcomes.	Socially embedded cognition

The variety of actors involves in shaping a technological regime (Geels, 2002)

The link between technological paradigms and trajectories

“Technological regimes result in technological trajectories, because the community of engineers searches in the same direction… [they] create stability because they guide the innovative activity towards incremental improvements along trajectories.” (Geels, 2002:1259)

“Technological trajectories “reflect cumulative efforts… [that] will possess a highish degree of momentum in certain directions’, such that ‘switching from the existing paradigm… to another aparadigm can be extremely difficult and also expensive.” (Clark and Staunton, 1989:109)

The trajectory for microprocessor density

Thomas Kuhn and discontinuity

Anomalies = evidence that does not fit prevailing paradigm. The persistent emergence of anomalies and the accumulation of adjustments to accommodate them, gives rise to increasing complexity and discrepancies within the prevailing theories of a field, whilst reducing their accuracy.

Crisis = scientists begin to lose faith in the current paradigm and start to consider alternatives.

‘Extraordinary Science’ = a change in attitude to the existing paradigm that manifests itself in a willingness to be more experimental and less constrained by the paradigm, by discontent, debate and disagreement as well as a rise of competing versions, of the ‘core’ theories and laws.

Technological discontinuity – a shift from the S-Curve of an old technology (on the left) to the S-Curve of a new technology (on the right) (Foster, 1986:102)

Technological discontinuity in the computer industry

Man-made fibres

Combustion

Technological discontinuity

“Creative Destruction is the essential fact about capitalism… it is not [price] competition which counts but the competition from… the new technology… competition which strikes not at the margins of the profits, of existing firms but at their foundations and their very lives.” (Schumpeter, 1942: 83-4).

“Those rare, unpredictable innovations which advance a relevant technological frontier by an order of magnitude and which involve fundamentally different product or process design and that command a decisive cost, performance, or quality advantage over prior product forms.” (Tushman and Rosenkopf, 1992:318).

Sources of discontinuity

Triggers / sources of discontinuity	Explanation	Problems posed
New markets	Most markets evolve through a process of growth, or segmentation. But at certain times completely new markets emerge which cannot be analysed or predicted in advance or explored through conventional market research / analytical techniques.	Established players don’t see ti because they are focused on their existing markets. Players may discount it as being too small or not representing their preferred target market. Orgininators of new product may not see potential in new markets and may ignore them.
New technologies	Step change takes palce in product or process technology – it may result from convergence and maturing of several streams (e.g. industrial automation, mobile phones) or as the result of a single breakthrough (e.g. LED as white light source.)	Established players don’t see it because  it is beyond the periphery of technology search environment. Tipping point may not be a single breakthrough, but convergence and maturing of established technological streams, whose combined effect is underestimated. “Not invented here” effect – new technology represents a different basis for delivering ‘value’ – e.g. telephone vs. telegraphy.
New political rules	Political conditions which shape the economic and social rules may shift dramatically – for example, the collapse of communism meant an alaternative model, and many ex-state firms coulnt modify their ways of thinking	Old mindset about how business is done is challenged and established firms fail to understand or learn new rules
Market Exhaustion	Firms in mature industries may need to escape the constraints of diminishing space for product and process innovation and the increasing competition of industry structures by either exit or by radical reorientation of their business	Current system is built around a particular trajectory and embedded in a steady-state set of innovation routines which mitigate against widespread search or risk taking experiments.
Sea change in market sentiment or behaviour	Public opinion or behavior shifts slowly and then tips over into a new model – for example, the music industry is in the midst of (technology-enabled) revolution in delivery systems.	Established players don’t pick up on it or persist in alternative explanations – cognitive dissonance – until it may be too late.
Deregulation / shifts in regulatory regime	Political and market pressures lead to shifts in regulatory framework and enable the emergence of a new set of rules – e.g. liberalization, privatization or deregulation	New rules of the game but odl mindsets persist, and existing player is unable to move fast enough or to see new opportunities opening up.
Fractures along ‘fault lines’	Long-standing issues of concern to a minority accumulate momentum (sometimes through the action of pressure groups) and suddernyl the system switches / tips over – for example, social attitudes to smoking or health concerns about obesity levels and fast-foods	Rules of the game suddernly shfit and then new pattern gathers rapid momentum, wrong-footing existing players working with old assumptions. Other players who have been working in the background developing parallel alternatives may suddernly come into the limelight as new conditions favour them
Unthinkable events	Unimagined and therefore not prepared for events which – sometimes literally – change the world and set up new rules fo the game	New rules may disempower existing players or render competencies unnecessary
Business model innovation	Established business models are challenged by a reframing, usually by a new entrant who redefines/reframes the problem and the conseuquent ‘rules of the game’	New entrants see opportunity to deliver product/service via new business model and require rules – existing players have at best to be fast followers
Shifts in ‘techno-economic paradigm’ – systemic changes which impact whole sectors or even whole societies	Change takes place at system level, involving technology and market shifts, This involves the convergence of a number of trends, which results in a ‘paradigm shift’ where the old order is replaced.	Hard to see where new paradigm begins until rules become established. Existing players tend to reinforce their commitment to old model, reinforce their commitment to the old model, reinforced by ‘sailing ship’ effects.
Architectural innovation	Changes at the level of the system architecture rewrite the rules of the game for those involved at component level	Established players develop particular ways of seeing and frame their interactions – for example, who they talk to in acquiring and using knowledge to drive innovation – according to this set of views. Architectural shifts may involve reframing but at the component level it is difficult to pick up the need for doing so – and thus new entrants better able to work with new architecture can emerge.

Competence-destroying and enhancing discontinuities

Abernathy and Clark (1985) distinguish between:

Competence-destroying discontinuities that “significantly advance the technological frontier, but with a knowledge, skill, and competence base that is inconsistent with prior know-how”

Competence-enhancing discontinuities that “significantly advance the state of the art yet build on, or permit the transfer of, existing know-how and knowledge.

The cycle of technological development and discontinuity (adapted from Anderson and Tushman, 1991:47)

Technological evolution and fragmentation in telegraphy

The Management Implications of Technological Fragmentation

Proliferation of new technologies causes:

-       Increasingly stretched resources;

-       Eventually not possible to be competent in all areas of technology in-house;

-       Brings about the need for technology transfer, licensing, and / or collaboration.

Transition and transformations in technological regimes

-       pressure from above (‘sociotechnical landscape’)

-       pressure from below (‘niches’);

-       pressure from within (sector); and

-       pressure from outsite (new science or other sectors)

The sources of pressure in changing a technological regime

Main actors and (inter)actions in transition pathways

Transition pathways	Main actors	Type of (inter)actions	Key words
1. Transformation	Regime actors and outside groups (social movements)	Outsiders voice criticism. Incumbent actors adjust regime rules (goals, guiding principles, search heuristics)	Outside pressure, institutional power struggles, negotiations, adjustments of regime rules
2. Technological substitution	Incumbent firms versus new firms	Newcomers develop novelties, which compete with regime technolgies	Market competition and power stuggles between old and new firms
3.  Reconfiguration	Regime actors and suppliers	Regime actors adopt component-innovations, developed by new suppliers. Competition between old and new suppliers	Cumulative component changes, because of economic and functional reason. Follow by new combinations, changing interpretations and new practices
4. De-alignment and re-alignment	New niche actors	Changes in deep structures create strong pressure on regime. Incumbents lose faith and legitimacy. Followed by emergence of multiple novelties. New entrants compete for resources, attention and legitimacy. Eventually one novelty wins, leading to destabilization of regime	Erosion and collapse, multiple novelties, prolonged uncertainty and changing interpretations, new winner and restabilisation

Economic and innovation long-waves

-       Far broader scope in relation to time, technology, and geography.

-       Represent attempts to maps cycle of economic performance and innovation.

-       Explores the possible symbiotic relationships between technological and economic systems.

Economic long waves (also known as ‘Kondratiew waves’)

e.g.

The Policy Implications of Schumpeterian Waves

-       Depends on acceptance of either the ‘prosperity-pull’ or ‘depression trigger’ hypothesis for the stimulus of radical innovation.

-       Recovery cycles arise from the dissemination of key new or ‘sunrise’ technologies.

-       Need to identify potential key ‘sunrise’ technologies and invest in basic research at national level is crucial, since competence around emerging technologies promotes future economic performance.

-       As existing competences in ‘sunset’ technologies become obsolete, this can signal a shift in economic power between nations (i.e. through discontinuity).

Concluding comments

-       The emergence and preservation of scientific and technological paradigms and technological are social phenomena.

-       Technological paradigms nad technological trajectories are inter-related.

-       Scientific and technological discontinuities can either enhance or destroy competence at the level of organization.

Innovation has been found to cluster at specific phases of the economic cycle, although there are competing explanati

Technology, Strategy and Organisation (Day 2)

Audio Sessions from TSO day 2

Session 2-2

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Session 2-3

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Product	Innovator	Follower	The winner
Jet airliner	De Haviland (Comet)	Boeing (707)	The winner
Float glass	Pilkington	Corning	Leader
X-ray scanner	EMI	General Electric	Follower
Office PC	Xerox	IBM	Follower
VCRs	Ampex/Sony	Matsushita	Follower
Diet Cola	R.C. Cola	Coca-cola	Follower
Instant camera	Polaroid	Kodak	Leader
Pocket calculator	Bowmar	Texas instruments	Follower
Microwave oven	Raytheon	Samsung	Follower
Plain-paper copier	Xerox	Canon	Not clear
Fibre-optic cable	Corning	Many companies	Leader
Videogames consoles	Atari	Sony/Nintendo/Microsoft	Followers
Disposable diaper	Proctor and Gamble	Kimberley-Clark	Leader
Web browser	Netscape	Microsoft	Follower
MP3 players	Diamond multimedia	Apple/Sony/others	Followers

Paradoxical nature of strategy

“At the heart of every set of strategic issues, a fundamental tension between apparent opposites can be identified… Each pair of opposites… seem to be inconsistent, or even in compatible, with one another… If firms are competing, they are cooperating. If firms must comply to the industry context, they have no choice. Yet, although these opposites confront strategists with conflicting pressures, strategists must somehow deal with them simultaneously.” (De Wit and Meyer, 2004:13)

Different types of tension in organizational decision-making

-       Puzzle = challenging problem with an optimal solution.

-       Dilemma = vexing problem with two possible solutions, neither of which is logically the best. They confront problem solves with diffulcut either-or choices.

-       Trade-off = problem situation in which there are many possible solutions, each striking a blance between conflicting pressures

-       Paradox = a situation in which two seeming contradictory, or even mutually exclusive, factors appear to be true at the same time. It is a problem with no real solution, or one which is internally consistent. Thus paradox can be characterized as a ‘both-and’ rather than ‘either-or’ problem, where e the solution requires the accommodation of opposites.

Paradoxes in the management of innovation

The following are in constant tension:

-       Planned versus Emergent (strategy & projects)

-       Top-down versus bottom-up (strategy)

-       Formal versus informal (structures & processes)

-       Exploration verssu exploitation of ideas

-       Radical versus incremental innovation

-       Internal (in-house versus external (outsourcing)

Managing multiple innovation strategies

Organizations may follow:

-       Different strategies at different times

-       Different strategies in different sectors

-       Different strategies in different divisions

Key questions

-       What patterns exist in the life of a technology and/or innovation?

-       What models exist to help explain and map such patterns?

-       To what extent are such models useful?

-       What are the implications of strategy-making

Models to be discussed

1. The ‘Technology Life Cycle’ (TLC) model – this maps the sales volume trajectory of a technology over time.
2. The ‘Technology S-curve’ model – this maps the technical performance trajectory of a technology in relation to research and development (R&D) effort.
3. The ‘Product-Process Cycle’ model – this maps the interrelated trajectories over time of product and process innovations within a sector.
4. The ‘Dominant Design’ model – this maps the emergence of a dominant design of an innovation or technology over time.
5. The ‘Diffusion Curve’ model – this maps the diffusion trajectory of an innovation ortechnology over time.

Features of models

-       All of the models are dynamic in nature;

-       Each can be grouped according to whether it attempts to map the ‘market’ performance trajectory or the technical performance trajectory of a technology;

-       Each typically adopts either a cumulative or a non-cummulative approach to expressing and mapping progress

-       Each model may be classified in relation to the four ‘motors of change’

The ‘Technology Life Cycle’ (TLC) Model

Mapping the sales volume trajectory of a technology over time

The ‘Technology S-Curve’ model

Mapping the technical performance trajectory of a technology in relation to research and development (R&D) effort.

Initial effort doesn’t provide performance returns due to the ground work, back ground knowledge and research. Performance is measured against a metric related to the product rather than knowledge accumulated.

Exponential growth

S-Curve Model Examples

S-Curve for the automobile	Series of shifting S-Curves of Semiconductor Technology
S-curve for Intel microprocessor speed	S-curve for Intel microprocessor density

Over view of the S-Curve model

Key Features	The model highlights the changing relationship between R&D effort (inputs) and improvements in the technical performance of a technology (outputs) over its lifecycle. The ‘technical limit’ and the technical potential (i.e. the gap between the current position of the curve of an organization or the state-of the-art and the technical limit) are key elements of the model.
Implications for managing innovation	Early on the life of a technology, patience is required, because R&D effort yields are low as the organization and sector build foundational knowledge and skills. The diminishing returns on R&D are important for signaling the narrowing of technical potential and the approach of the technical limit, and thus are key for highlighting the need to look for alternative technologies
Critique-limitations	It represents a fairly crude input-output model, which black boxes’ important aspects of process and context. It is also very difficult to determine or measure with any accuracy they key elements of the model, such as the technical limit or the current position on the curve of the organization or state-of-the-art
Critique-complications in application	The important performance criteria for a technology are subject to change and are often in tension with one another. A technology may be compromise of

The ‘Product-Process Cycle’ Model

Mapping the interrelated trajectories over time of product and process innovations within a sector

Innovation characteristics linked to phases of the product-process cycle

Innovation characteristic	Fluid pattern	Transitional phase	Specific phase
Competitive emphasis placed on…	Functional product performance	Product variation	Cost reduction
Innovation stimulated by…	Information on user needs, technical inputs	Opportunities created by expanding internal technical capability	Pressure to reduce cost, improve quality, etc.
Predominant type of innovation	Frequent major changes in products	Major process innovations required by rising volume	Incremental product and process innovation
Product line	Diverse, often including custom designs	Includes at least one stable or dominant design	Mostly undifferentiated standard products
Production processes	Flexible and inefficient – aim is to experiment and make frequent changes	Becoming more rigid and defined	Efficient, often capital intensive and relatively rigid

The reverse product-process cycle

An overview of the product-process cycle model

Key Features	The product-process cycle incorporates three phases, each with particular patterns of innovation, competition, industry structure, and organization The ‘fluid’ phases; The ‘transitional’ phase; and The ‘specific’ phase. During the life cycle, there is a shift from product to process innovation, major improvement to incremental innovation, entrepreneurial to large ‘mechanistic’ organizations, and competition based on differentiation to that based on cost.
Implications for managing innovation	At the organizational level, each phase of the model requires a different strategic orientation, centred on the balance between innovation and efficiency; and a different mode of organizing, concerning the balance between the ‘organic’ and mechanistic’ modes as well, as different competences and resources. At the sectoral level, different market conditions and competitive pressures characterize each phase of the model.
Critique-limitations	The original model is ‘unidirection’ and ‘irreversible’, however, subsequent versions allow for de-maturity during the ‘life course’ rather than ‘life cycle’ of a technology. The model has little efficacy when applied to a service sector, hence the development of reverse product-process cycle. It underemphasizes problems of transition for incumbent organizations
Critique-complications in application	Identifying the revolutionary transition between phases of the model is problematic, in part due to the ‘fuzziness’ of their boundaries. It is difficult to apply to complex products, such as computer, automobiles, and aircraft, which are comprised of nested levels of technology.

The ‘Dominant Design’ Model

Mapping the emergence of a dominant design of an innovation or technology over time.

Stretching the dominant design through incremental innovation

Incremental innovation in:

-       Handlebar arrangement

-       Saddle position

-       Huge gear

-       Short wheelbase

-       Riding position

… all lead to a very aerodynamic bicycle

Factors influencing the emergence of a dominant design

-       Relative marketing and advertising expenditure

-       Relative scope and scale of the distribution channels

-       Relative brand (corporate and product) strength

-       Degree of standard setting – influenced by regulation and/or industry cooperation

Shaping industry standards – the case of HDDVD and Blu-Ray

<insert notes>

Management implications of the emergence of a dominant design

-       Diffusion mechanisms and standards setting play an important part in the emergence of a dominant design;

-       Late entrants to the market are unlikely to be able to comete directly with the dominant design;

-       Innovation opportunities exist in stretching the dominant design (the improvement of components, accessories and material) or developing niches.

An overview of the dominant design model

Key features	In the early phase of a new technology, a range of divergent designs are present. After a time, however, there is a ‘shakeout’ as the market converges around one or a small number of designs that eventually dominate. Opportunities remain in relation to ‘stretching’ the design through innovation in materials and components, or in the development of new design families
Implications for managing innovation	The emergence of a dominant design or industry standard in a market signals the ending of a period of technological turbulence and competitive ferment. With the emergence of a dominant design comes the narrowing of opportunities for new entrant, and the need for incumbent organizations to influence the choice of design in the market and/or the industry standard.
Critique – limitations	The model is ‘unidirectional’, despite the recognition of niche opportunities. The model itself says little about why or how dominant designs emerge. But subsequent research has highlighted non-technological factors, such as developing loyalty among the user base, building wide distribution networks, and influencing standard-setting processes.
Critique – complications in application	It is time-consuming to apply to complex products, such as computer, automobiles, and aircraft, which are comprised of nested levels of technology; dominant designs may exist within each of these levels, and emerge ad different times and rates.

The ‘Diffusion Curve’ Model

Mapping the diffusion trajectory of an innovation or technology over time.

Foster 1986

The innovativeness dimension, as measured by the time at which an individual adopts and innovation or innovations, is continuous. The innovativeness variable is partitioned into five adopted categories by laying off standard deviations from the average time of adoption (x). The model is useful for reporting, but not for forecasting.

• Innovators – Recognise the early market needs
• Early Adopters – Centre of communication ‘Free advertising’
• Early Majority – Momentum building
• Late Majority – Followers
• Laggards – Don’t care, objectors

Factors influencing diffusion innovation characteristics

-       Relative advantage – the degree to which an innovation is perceived by potential adopters to be ‘better’ than alternatives;

-       Compatibility – the degree to which an innovation is perceived by potential adopters to be aligned or consistent with their prevailing needs, values or experience

-       Complexity – the degree to which an innovation is perceived by potential adopters to be difficult to understand or use;

-       Trialability – the degree to which an innovation can be experimented with by potential adopters prior to adoption;

-       Observability – the degree to which the benefits of an innovation can be observed by potential adopters

EXAMPLE: Diffusions and the case of virtual reality

Limitations

• No accounting for external factors (social, political, fuel prices, regulators)
• No interdependence
• Reporting – not forecasting
• Performance improvements / Functionality increase

Transitions between

Innovators -> Early Adopters

• Relevant marketing / advertising
• Distribution Channels
• Policy and Standards
• Brand Strength
• Social Networks

Different shapes for the product life cycle

An overview of the diffusion curve model

Key features	The diffusion curve represents the typical diffusion pattern for an innovation. The model identifies five adopter categories, as well as five innovation attributes, which help to identify features of both users and innovations that impact the speed of diffusion/adoption of an innovation.
Implications for managing innovation	Different adopter categories have different roles to play in the diffusion adoption process, as well as in the innovation process. Shaping the innovation with regard to the five innovation attributes identified will help promote the rate of diffusion/adoption of an innovation.
Critique-Limitations	Without reliable market data for the potential groups that are likely to adopt the innovation, and thus the overall potential number of adopters, the diffusion curve model essentially becomes relegated to a reporting tool rather than a forecasting tool. The model does not explicitly take account of subsequent major improvements in the performance, cost, or functionality of the innovation that might alter the pattern of its diffusion.
Critique – complications in application	The diffusion pattern of ‘interactive’ innovations, such as the telephone, fax, email, and teleconferencing, differs from the diffusion pattern of discrete or non-interactive innovations. An innovation may be adopted, rejected, adopted and adapted, or adopted and then later abandoned.

An overview of the key features of the models

Model	Introduction stage	Growth stage	Maturity stage
Technology S-Curve	The rate of improvement in the technical performance of a technology is slow, because much of the R&D effort is required to develop the basic knowledge underpinning the technology. As a result, the technical performance returns from R&D effort are low.	As knowledge about a technology accumulates and is diffused and applied, the rate of progress in the improvement of a technology begins to accelerate. Here, the technical performance returns from R&D effort are high. But after the ‘point of inflection’ (the middle of the S-Curve) at which the yield is at its highest, the innovator begins to suffer from decreasing returns.	The yield on R&D effort begins to decline at an increasing rate as it approaches the ‘natural’ or ‘technical’ limits of the technology.
Product-process cycle	Associated with the emergence of a new market need or a new way of meeting an existing market need. Initially, in meeting this need, there is often vagueness concerning the appropriate performance criteria of the new product offering. As a result this phase is characterized by frequent major changes to products as well as diversity of products in the market place.	In order to meet rising demand for the new product, there is a shift away from frequent major product innovation to major process innovation. This phase is associated with emergence of one or a small number of stable product designs to allow significant production volumes, and the development of ‘islands of automation’.	There is a focus on cost reduction and product quality, and a shift towards incremental product and process innovation to bring about such improvements. The gradual and cumulative impact of ‘countless’ minor incremental product and process innovations can have a dramatic impact on productivity.
Dominant Design	The emergence of a new technology leads to a great variety of competing innovations with divergent designs in delivering the technology to the market place	There is a shakeout among the divergent set of design initially available in the marketplace, followed by the evolutionary improvement of a narrow range of ‘composite’ designs.	As the market matures, an even narrower set of consolidated designs emerges, with the possibility of the emergence of a single dominant design. Once establish in the market place the dominant design can then be ‘stretched’, through the development of new materials, components, and accessories, or through the development of the design families
Diffusion curve	‘Innovators’, with a keen interest in new idea, are the first to adopt and innovation. They are a small minority of the overall group 9i.e. only about 2.5%). Early adopters’, unlike innovators are embedded in their social system, they are ‘localities’ rather than ‘cosmopolites’. This group represents a large proportion of the overall group, although still a minority (i.e. around 13.5%)	The ‘early majority’ are individuals who adopt an innovation just before the average person. They make up about one third of the overall group. Although they take long to deliberate before adopting or rejecting an innovation than innovation and early adopters, they have an important role to play in the diffusion process, through creating momentum in the diffusion of the innovation and social pressure on non-adopters to adopt, and help to build a critical mass	The ‘late majority’ are individuals who adopt innovations after the average person. They also make up about one third of the overall group. This group tends to be cautious or skeptical of new ideas. Often, peer pressure is an important stimulus for this group to adopt. ‘Laggards’ are the last to adopt. They constitute about one sixth of the overall group. They are generally suspicious of new ideas and change, and their point of reference tends to be the past.

Conclusions

-       Such models allow managers to view their industry from an historical and temporal perspective;

-       They provide the language and tools for discussing the past, current, and future technological environment; an important prerequisite to the formulation of strategy

-       However, such models are overly deterministic, and underplay the potential for organizations to influence the shape of, and speed of movement along, a trajectory;

-       There is an over emphasis on the ‘supply-side’.

Extra reading: Managing and shaping innovation – the patterns of innovation within the life cycle of a technology

April Activity

This month has been insanely busy as University work and some industrial conferences eat all the time I have. The Technology Strategy and Organisation (TSO) module has an exam that requires some focused revision. It’s been years since I’ve had to do any kind of exam and while the theory of taking exams is still in there, the cramming doesn’t get any easier.

I also had the idea that the systems centre could become a great showcase of UK systems engineering and that an installation of Oblong, made famous by the film ‘Minority Report’ could be an interesting collaborative environment and ‘jewel in the crown’ for the centre; Oblong Industries g-speak spatial operating system

University

• EngD Lectures; Exam Revision Technology Strategy and Organisation (TSO) (Bath);
• Literature Review
• Find additional modules; Royal Hollaway Uni; Pen Testing & Security Management
• Academic Meetings; 7th, 21st April
• Supervision; 1 Rasmus student (Systems Engineering), 2 MSc Students (DoS, Modelling)
• Possible purchase of Oblong presentation system for Systems Centre as a collaborative environment
• RASMUS Student research, survey and draft paper

Company

For confidentiality I obviously snip the sensitive parts from this activity log, but try to give an idea or theme of what I get up to as an EngD research engineer. This month was traveling to/from conferences and cramming for the TSO exam.

OTHER

• Defence IT 2011 Conference
• InfoSec 2011 Conference
• Future Ideas generation

Other

• Public Engagement – System Centre day for GCSE/sixth form students

February Activity

I have found the library, so now progress on my project means getting a stack of books out and leaving them on my desk at work – that should fool people into thinking I read them. I was surprised to find some great books for my project, that seem to have made all the recommendations I have been thinking of, but are TEN YEARS OLD! Worse than that, I seem to be the second person to take these books out. First date was back in Feb 1991, and now I take them out in Feb 2011.

As my project can be a ‘roadmap’ of projects, I could replicate the work carried out that looked into current legislation requirements, existing policy, technology capabilities, organisational responsibilities, etc. I still need to keep in mind the academic rigour and contribution of new knowledge requirement of the EngD as it is easy to find interesting work to do. The fundamental component of the EngD is the PhD research. I could waste four years doing work for the company, but come away with nothing for the EngD (EngD:Loss). I could conduct a usual PhD project that churns out papers, but provides no value to the company – I would still get the EngD award, but the company wouldn’t see a return on investment and would not sponsor another EngD research engineer (EngD:Loss). To get the EngD:Win, is to find a balance that allows academic contribution, but also provides value to the company (usually some economic advantage through a new process/product/knowledge).

University

• EngD Lectures; 17th Feb Risk Perception, Psychology and Economics of Information Security
• EngD Coursework; Due 11th Feb
• Academic Meeting; 17th Feb,
• Arrange meeting with Elke Krahmann (UniversityofBristol) 4th Feb
• Literature Review

Company

• Cyber things
• System Engineering things

January Activity

Have settled into the Company and University, but still need to work on the Employee-Student balance. All the time is spent at the company with the odd day (mostly Friday) being the ‘Uni day’ to allow me time to keep the environments apart. I can’t do any science paper reading at the company due to distractions and the fact I haven’t worn a shirt and tie so much since skool.

University

• EngD Lectures; none
• EngD researching possible additional modules (Psychology, Cyber Security or even a language)
• Academic Meetings; 12th January
• System Centre Meetings; 11th January
• Cryptography group meetings; 5th ‘DoS-resistant key exchange’, 25th ‘leakage-resilient cryptography’, 26th Debi Ashenden (Cranfield)
• Started a (wide) Literature Review.
• Reading ‘Systems thinking, systems practice’ – Peter Checkland

Company

• Unstructured Interviews with team and management
• Conference 13th Jan
• <snip>

Other

• Joined professional organisations; INCOSE and Institute of Information Security Professionals (IIPS)
• Renewed membership to organisations; IET and BCS