Contrary to common belief about what it takes to succeed with technologies, innovation decision highly matters. The rise and fall of firms largely depend on innovation decisions for exploiting latent technology possibilities. For example, despite having superior capabilities than either Intel or Microsoft, IBM decided to source the operating system (OS) to Microsoft and the microprocessor to Intel. Similarly, upon securing the first digital camera patent, Kodak chose not to pursue it; but Sony took the opposite innovation decision–creating the Kodak moment.
Consequentially, Kodak and IBM fell behind, while Intel, Microsoft, and Sony kept rising. Ironically, down the road, Intel made a wrong innovation decision by rejecting Apple’s offer to produce a chip for iPhone, as Intel could not detect latent possibilities. On the other hand, TSMC in Taiwan decided to cater to the market of small orders of fabless chip companies for printing their designs on the wafer. As a result, despite superior technology capabilities, Intel has fallen behind TSMC.
Often, in discussing innovation-decision, we refer to Rogers’ theory of individuals’ mental model or attitude about an innovation, leading to a decision of adoption or rejection. It seems that Rogers’ innovation-decision process refers to the diffusion pattern of innovation as the public good. But innovation decision for exploiting latent technology possibilities is far more important and different than that. It refers to understanding the latent possibilities and decisions of nurturing and exploiting for profit-making purposes in a competitive market. For example, Xerox management rejected the proposition of exploiting graphical user interfaces for computers. But Apple made a massive success by deciding to develop it further and commercialize it. Innovation decision-making is challenging because possibilities are fraught with pervasive uncertainties.
Pervasive uncertainties make innovation decisions a complex exercise
Conventional rational decision-making processes, based on statistical probabilities or social theories, are inadequate for innovation-decision to exploit technology possibilities. The underlying cause has been pervasive uncertainties. It begins with latent technology possibilities. The remaining six categories belong to (i) consumer preferences, (ii) competition and business model, (iii) ecosystem, (iii) externalities, (v) spillover effects, and (vi) public policy and regulation.
To deal with these uncertainties, we need to select and adapt an appropriate framework in the form of reoccurring patterns. This model will help comprehend and predict wealth creation dynamics out of technology possibilities in a competitive market. In the absence of a framework, conventional innovation decision-making (IDM) as an individualized, self-assessment, and workshop-based methodology is ineffective. Due to it, rational decision-making step-wise process by following a logical, data-driven manner is also useless. Hence, we need to look for reoccurring patterns by investigating pervasive uncertainties.
- Technology progression
- Consumer preferences
- Competition and business model
- Innovation ecosystem and supply chain
- Externalities, infrastructure, and compatibility
- Spillover effects
- Public policy and regulation
Innovation Decision Faces Technology Uncertainties
Technology characteristic is supposed to be specific, having defined input-output relations. But within the context of innovation decisions, there are many uncertain aspects of technologies. Some of the underlying reasons for creating uncertainties in exploiting technology possibilities are:
- Primitive emergence of technology possibilities
- Loss-making beginning and uncertainty in reaching profitability
- Unclear, unpredictable, and unfolding possibilities and spillover effects
- Increasingly costly experimentation to figure out growth trajectory
- Uncertainty in loss and expected profitability
- Uncertain product life Cycle and growth pattern
- Unpredictable stock price and firms’ market value: linked with technology possibilities
- Demanding Increasing Scientific Discoveries
- Competing multiple technologies
- Misleading early progress: extrapolation fails to predict and risks misguidance
All technology possibilities fueling innovations emerge in primitive form. Starting from transistors, word processors to microprocessors, there have been numerous examples. In the beginning, despite showing unique characteristics, invariably, they show up as an inferior alternative. For instance, the inventions of LED or electronic image sensors got birth as an inferior alternative to their mature counterparts. But they have unique characteristics. For example, electronic image sensors could produce images instantly without requiring intensive chemical processing.
Similarly, LED was far more energy efficient than incandescent lamps. But none of them was right away better than their mature counterparts. As the potential remains latent, they offer uncertain innovation possibilities. Hence, the focus should be on underlying science and engineering to assess the evolution, leading to scalability. Furthermore, due to faint capability, any innovations out of them roll out as loss-making ventures. As the growth potential remains latent and loss-producing beginning, innovation decision faces the challenge of estimating the duration and R&D cost needed to turn the loss into profit.
Consumer preferences risk innovation decision
Upon investing $25 billion, Airbus succeeded in delivering a super jumbo jet—A380. Concerning technology and engineering excellence, it was a marvel. But after more than a decade of flying, Airbus encountered an unpleasant reality. Consumers like shorter point-to-point flights to more extended luxury flights through hubs. Hence, A380 became costly, as airlines were experiencing partial occupancy in flying their super jumbo jets. Consequentially, Airbus decided to stop producing the A380 before recovering any dollar of the massive $25 billion R&D investment.
Similarly, after spending $500 million in R&D over 25 years on humanoid ASIMO, Honda learned that elderly people did not like robot nurses. There have been many examples of innovation failure due to uncertainty in consumer preferences. As many conventional means, like showing for verifying or listening to customers, have minimal efficacy, the focus should be on empathy, passion for perfection, and reoccurring patterns for increasing prediction accuracy.
Innovation decisions face uncertain competition and business model
Upon traversing a long loss-making path of evolution through R&D, once innovations start showing profitability, they face competition force. For taking away a slice of unfolding opportunity, competition responds. Responses show up as (i) replication, (ii) imitation, (iii) innovation, and (iv) new business model. Sometimes, it shows up as a substitution. For example, although Kodak rejected the digital camera, it got into it upon seeing the growing adoption of Sony’s digital imaging innovations.
Similarly, the evolution of Motorola’s Dynatec enticed responses from Nokia, Ericson, Siemens, and many others to release their mobile handsets. Another form of competition response that innovation decision faces is business model innovation. For example, Google, Facebook, and many others showed up with three-party business models in Internet innovation.
Innovation ecosystem and supply chain
No company can afford to produce all the components needed to roll out innovations. For example, even Apple sources components from more than 200 suppliers to support the evolution and making of the iPhone. In the absence of Sony’s progress in camera, Apple cannot release the next significant feature. Hence, innovation decision faces the challenge of building an innovation ecosystem through loosely coupled partnerships. Hence, innovation decision is a considerable challenge. Like Apple, the success of more or less all innovators like ASML, Toyota, or Oracle depends on the formation of the globally distributed innovation ecosystem. Such a reality demands a highly synchronized supply chain to support the timely sourcing and production of innovations.
Externalities, infrastructure, and compatibility
Both positive and negative externalities affect the outcome of innovation decisions. For example, the 3rd party component plugin through App stores has played a solid positive externality in Apple’s iPhone innovation decision. Similarly, competition among mobile equipment makers and operators in expanding the reach and speed of mobile internet has been a tremendous positive externality to smartphone innovations. But innovation decision also faces negative externality. For example, growing e-waste has been impeding the diffusion of digital gadgets.
On the other hand, the lack of infrastructure rollout, such as charging stations for EVs, negatively affects innovation diffusion. For sharing infrastructure, there is also a need for standardization and compatibility. Hence, innovation decision also faces the reality of synchronized responses by multiple actors to externalities, infrastructure, and compatibility.
During the evolution of technology possibilities, unforeseen opportunities start unfolding—spillover effects. Often, the prediction and exploitation of such spillover effects are beyond the reach of a single innovator. Hence, the total economic benefit from innovation decisions is difficult to estimate and exploit by a single actor. In such a case, a synchronized response by multiple innovators and actors, including the Government, may strengthen innovation decisions.
Public policy and regulation affect innovation decision
Both negatively and positively, public policy and regulation affect innovation decisions. For example, due to stringent emission regulations, the determination of electric vehicle innovation has been beneficial. On the other hand, the recent US Government policy decisions have negatively affected the export of many semiconductor equipment makers like ASML, Lam Research, and KLA. But the same policy framework is helping fab investors like Intel. Hence, innovation decisions should also consider unfolding policy and regulatory issues.
As explained, innovation decisions are fraught with pervasive uncertainties. Even if it requires Nobel Prize-winning scientific discoveries to overcome the last mile hurdle. For example, turning the innovation decision to pursue the LED bulb by Nichia required a scientific discovery, which won the Nobel prize. On the other hand, upon reaching success after investing billions over a decade, the innovation decision of ASML faces the barrier from public policy in exploiting the benefit. Hence, due to the immense importance of diverse issues affecting the outcome of innovation decisions, we should expand our understanding of what it takes to succeed with creativity, engineering, and technology possibilities.