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

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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