Over in this article we see that, on the one hand, innovation in services is similar to product innovation. Yet, on the other hand, there are additional factors to address in services innovation. This is good news, as it means product and services innovation share some common factors to address.
What are these common factors? Let’s explore…
- Product innovation: creating new or enhanced products
- The main challenge is diffusion/adoptability
- A firm bakes value into a product through its act of manufacturing
- Value creation is seen as a firm specific attribute – the firm’s wealth is increased through exchanging the product for cash
- The end customer uses up/destroys the value baked into the product (there is no co-value generation)
- Most current innovation theory and practices are based on innovating products/goods in manufacturing industry
What is a product?
First, let’s look briefly at what products are, so we’re agreed we are talking about the same thing.
Products are all around us. They are items we can touch, transport and store for future use. More importantly, we sell products, exchanging tangible items for, usually, cold hard cash. These products are manufactured by firms, typically by combining products from other firms into a new form and factor. In doing so, firms embed value into the new product. For example, a car is generally more valuable than its component parts.
Once a customer has purchased a product, the producing firm has little more involvement (except in cases of warranty). The customer, however, goes on to use up or destroy the embedded value by using the product. A car wears out through usage, and the value embedded in a chocolate bar is rapidly used up when eaten (almost instantly if in my hands!).
What I have described above is part of what we call Product Dominant Logic (PDL). In Figure 2 you can find the main differences between PDL and how we need to think about services. If you are interested in services, and I hope you are, then I look at them more in this article.
(Just a quick word of clarification: some fields, particularly marketing, use the term product to refer to both goods and services. Goods, in that case, is the same definition I used earlier for product. If I don’t explicitly say otherwise then I mean products and services to be separate things)
Now we have an idea of what a product is, let’s take a look at product innovation.
What do we do in product innovation?
When we innovate in products, we are typically innovating to:
- enhance attributes of an existing product – for example adding a 9th razor blade in the cartridge, or creating/embracing complementary products
- develop new products – a novel way to remove hair, for instance
- shift our business model towards services
We have a tendency to assume that any product innovation is a good thing, and our challenge is “just” to get customers to know about it and buy it. This bias is recognised by Rogers (1983) as “pro-innovation bias”. A good, and funny, example of this bias can be seen in this Ali G’s ice-cream glove product innovation video.
Luckily, researchers have studied (product) innovation a lot. They have spent countless hours observing innovation in manufacturing industries and developing theories and models of how it works. The output of this is what I call product-dominant innovation thinking. And it is this thinking that is predominant in books, most innovation tools, and consultants selling you their innovation approaches. (As you’re reading this article, you probably already know I feel this contributes to the innovation problem. Since our economies are 80+% service-based. However, I also see service innovation as having additional attributes to address than product innovation, rather than being completely different. So it is still very useful to have a good understanding).
In this product dominant knowledge I include the following:
- Product-dominant logic (see above)
- Ideation and ideation tools
- Adoptability and diffusion of innovations
- Variables affecting the rates of adoptability
- Ideation tools
- Portfolio management
- Innovation chains
- Lean, agile,…
All of these are noble and highly useful concepts. And they work very well in a product-dominant industry/mindset/economy. Luckily, they also form a necessary subset of the thinking required for service-dominant innovation.
With that in mind, I will take us on a quick run through these aspects.
Ideation is a word whose meaning has been referred to as jargon. A word whose dictionary definition is that of a noun and focussed to “the formation of ideas and concepts” often linked to the clinical term suicide ideation (oxford dictionaries, Merriam-webster).
Yet, in innovation circles, it has come to be used as a verb: the creative process of coming up with new ideas. There are many approaches – free-thinking brainstorming sessions; capturing ideas on post-it notes; call to actions responded to via web tools; design thinking; and so on.
Sometimes ideation tools offer an idea/innovation management functionality. These tools help filter a hopefully wide set of ideas that have been collected into a smaller set that a company is interested in taking forwards.
It’s important to always remember that an idea is not an innovation. It is just that: an idea, the act of creativity. But it’s an important step!
Of course, both product and services need this creativity in order to drive opportunities for innovation.
Innovation Adoptability / Diffusion
Diffusion: “the process by which an innovation is communicated over time among the members of a social system.”Rogers, 1983
As we saw above with Ali G’s ice cream glove, we often suffer a pro-innovation bias. That is to say, we often believe our product innovation is a good thing. And that the only obstacle to vast profits is getting customers to be aware of, and to buy, the product.
From a literature perspective, we are talking about diffusion and adoption. And when we talk about those, we typically are referring to four things:
- Rogers’ adoption curve,
- Moore’s “Crossing the chasm”
- Bass’ model (basic and multi-generational innovations), and
- Variables affecting the speed of adoption
I’ll cover each of these briefly below. The first three I expand on in my article on innovation adoption, the fourth in my article on affecting the speed of adoption [link].
Let’s take first Rogers observations which resulted in his adoption curve.
Roger’s Adoption curve
Rogers’ reviewed the literature and identified that adoption of an innovation follows a typical path with pretty typical types of people adopting at particular times. The result is his adoption curve that I’ve reproduced in Figure X.
He found the following:
|Adopter Type||% of Market||Typical characteristics|
|Innovators||2.5%||Willing to take risks; typically closely related to the industry producing the innovation|
|Early Adopters||13.5%||Thought leaders/influencers|
|Early Majority||34%||Often connected with early adopters; includes your average customer|
|Late Majority||34%||Need to overcome a degree of scepticism before adopting|
|Laggards||16%||Typically resistant to change and hard to get on board|
The adoption curve is great to get an understanding of how your innovation typically diffuses amongst a population. Moore, however, found some challenges when it came to technology innovations.
Crossing the Chasm
Moore took Roger’s adoption curve and identified that particularly for technology products there is a challenge getting from the early adopters to the early majority. He refers to that as “crossing the chasm”
Moore comes from a marketing background and reposits Rogers’ segmentation of diffusion/adoption into market segments to which different marketing should be applied. He looks upon early adopters as enthusiasts and early majority as pragmatists. Crossing the gap is, he sees, a marketing problem to solve.
Another adoption curve is Bass’.
We can’t really talk about Roger’s adoption curve without talking about Bass’ model. Where Roger’s work is xxxx, Bass provides the mathematical foundations.
Bass’ diffusion formula has two coefficients, p and q. Co-efficient p is referred to as the coefficient of innovation. It relates to the number of new adoptions due to some influence that is external to the social system. Due to that, it is sometimes (and should be more often in my view!) called the “parameter of external influence”. The other coefficient, q, is the coefficient of imitation – adoptions influenced by the social system. That is to say, the more people talking about the innovation, the more others are likely to adopt. Again, due to this, it can be called the “parameter of internal influence”.
Initially, p is more important than q, as shown in Figure 3.
This reflects what we see in Rogers’ literature insight. The innovators and early adopters are influenced externally to adopt. As we move along the adoption curve, the influence shifts from external to the social system to internal; as innovators and early adopters start influencing the early majority, etc.
Bass extended his model to look at several generations of innovations. It still turns out remarkably predictive. Below we see different generations of computers, from kit computers back in the ’70s through powerful setups built for the internet generation.
Addressing rates of adoption
Roger’s adoption curve is great in allowing us to understand the different types of customers we have at different times in the diffusion/adoption of an innovation. We can, therefore, target different marketing campaigns at each segment to try and move along the timeline (getting more adoption, and therefore more value for our firm in terms of sales etc).
There are also ways to increase the speed of adoption. Roger identified 5 key variables shown in Figure X.
We will take a quick walk through each of the five in turn.
Perceived attributes of innovation
Rogers identified that there are 5 perceived attributes of an innovation that affect its rate of adoption / diffusion:
- Relative Advantage
relates to the degree that the innovation can be perceived as being better than what already exists. The higher that relative advantage is perceived then the higher the rate of adoption. For example, how much better the iPhone 20 is compared to the iPhone 19 is one factor in how quickly people will buy it or stay with their iPhone 19.
We can also bundle “value” and “fulfils a customer’s needs” under relative advantage.
If the innovation is compatible with the experiences, values and needs of the adopters, then it is likely to be adopted quicker. Typewriters had (have) a QWERTY layout to minimise the strikers jamming as people typed. There was no need for computer keyboards to cope with that problem, yet they are typically QWERTY based because of this “compatibility” perception.
The more complex an innovation is perceived to be, the lower the rate of adoption.
Tesla initially had a problem looking to sell an electric car to a population not familiar with electric cars. By creating their own showrooms and strongly controlling the experience, they allowed customers to trail their cars (even if not driving, you are able to get inside and climb all over it). Volvo has done a similar thing in Sweden by being part of a company called Sunfleet which has Volvo electric cars that can be picked up and dropped off at a large number of fixed stations across Stockholm. People get to trial Volvo’s electric cars.
Finally, the more people can observe an innovation in use, the more likely they are to adopt it themselves. Sometimes you have to think a little out of the box to get this observability. Ever wondered why Apple’s mobile devices come with white earphones? It is hard to observe people using iPhones if they are hidden in pockets, but the white earphone and cables were very obvious in the early years letting you see just how many people had iPhones.
The rate of adoption is also affected by the way the decision to use an innovation is made.
Type of Innovation-Decision
Nature of Social System
Extent of Change Agents’ promotion efforts
So product innovation is about embedding value in a product that is then used-up or destroyed by the customer.
For them to do that, the product innovation needs to be adopted and we saw that there are several segments of adopters that the product goes through in its life cycle.
Adoption rates are dependent upon five variables including perceived attributes and that an imitator is likely to have faster adoption rate than original innovator due to being a fast follower since the innovator has done the hard work of getting through the early segments of the adoption curve.
As we have mentioned a few times, service innovation needs to address the same factors as above, yet has additional factors to address.