EngD Blogs

I have been meaning to search for other EngD blogs, but despite the EngD running for a number of years, there seems to be only a few. Looking at the google stats between search terms ‘EngD blog‘ (144k results, 0.27 seconds) and ‘PhD blog‘ (41.4M results, 0.18 seconds) highlights how under represented the EngD programme is – no wonder we have to keep explaining the difference.

Clare Hooper
I’m soon to graduate from my EngD (conducted in the University of Southampton’s Learning Societies Lab, sponsored by IBM UK). I now work in the User Centered Engineering group at the Eindhoven University of Technology. My research interests include (but are not limited to) hypertext, web science and how we design software. My webpage lives here, and I’m also on Twitter.
Personal http://clarehooper.wordpress.com

EngD research blog at Southampton University

Jo Symington
I’m doing bits of marine microbiology, bits of molecular biology, bits of chemistry and bits of engineering and I’m working with an industrial partner Croda… and I’m technically a biologist (at least I think I remember my first degree being Applied Biology…) so how have I ended up doing an EngD???
EngD research blog Jo’s Eng-mare!!!

Ben Betts
There I am currently studying for an EngD (Doctor of Engineering), studying the role of new technology for learning within corporate environments. I started this blog to collect my thoughts for the EngD and to test out some theories, mostly to see what kind of response they generate. @bbetts
http://www.ht2.co.uk/ben/?page_id=2

Some blogs that have started

EngD Eppie

I’m Eppie, currently in my first year of the Engineering Doctorate in Biopharmaceutical Bioprocess Development at Newcastle University. Yeah, it’s a real long name and I’m not 100% sure I’ve even got it down right. We just call it the EngD.
http://engdeppie.wordpress.com

I’m going to keep an eye out, but let me know if I’ve missed any!

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Monday, 6th 06 Jun 2011

Filed under EngD

May Activity

No time to think this month as we not only revise for the TSO exam  (2^nd June, 1Day) but also have a lecture block ‘Maths for Systems’ with daily bits of coursework to be completed (and it really mounts up if not done) and a module assignment.

I also squeezed the six month review in that I should have scheduled earlier to get it out of the way. The meeting was really to tick a box and fill in some paper work for the systems centre. I get the feeling my industrial supervisor was expecting a report or  presentation, but I had to admit that with no time, I hadn’t prepared anything and this was going to be the first proper chat between us all about the research project. It was good to see that my academic supervisor was laid back and ‘happy’ with the level of progress made, which countered the business pressure to have an entire timing and requirements plan in place. I did come away with some action points and a better idea of a research direction that would benefit us all.

University

• EngD Lectures; Maths for Systems (16^th-20^th May)
• 6 Month Review (12^th May)
• TSO Exam (2^nd June)
• 2^nd Systems Centre Annual Conference (24^th – 25^th)
• Literature Review
• Supervision; 1 Rasmus student (Systems Engineering), 2 MSc Students (DoS, Modelling)
• 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 honestly, not a lot due to travelling 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

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Sunday, 29th 29 May 2011

Filed under EngD

Technology, Strategy and Organisation (Day 2)

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

Over view of the S-Curve model

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

The reverse product-process cycle

An overview of the product-process cycle model

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

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

An overview of the key features of the models

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

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Wednesday, 25th 25 May 2011

Filed under EngD

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.

[ti_audio media="375" repeat="1" volume="90"]

Session 1-5

[ti_audio media="372" repeat="1" volume="100"]

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

(image)

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

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

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Wednesday, 25th 25 May 2011

Filed under EngD

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

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),

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

“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

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

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

By: Richard Craig

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Thursday, 26th 26 May 2011

Filed under Audio | EngD | Reflections

Technology, Strategy and Organisation (Day 2)

Audio Sessions from TSO day 2

Session 2-2

[ti_audio media="374" repeat="1" volume="90"]

Session 2-3

[ti_audio media="376" repeat="1" randomize="1" volume="90"]

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

Over view of the S-Curve model

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

The reverse product-process cycle

An overview of the product-process cycle model

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

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

An overview of the key features of the models

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

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Wednesday, 25th 25 May 2011

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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

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Sunday, 1st 01 May 2011

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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

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Monday, 28th 28 Feb 2011

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