Despite the immense importance of Innovation, as high as 94 percent of executives are unsatisfied with innovation performance”. Management has been facing the challenge of understanding how innovations, notably High-tech ones, diffuse in the market. So far, Rogers innovation diffusion model has been a predominant guide for the management—even for marketing gurus.
Rogers’ innovation adoption curve reflects how different groups perceive the risk of technology and accordingly decide about its adoption. According to it, a person will “self-select” on an axis of risk, which starts with innovators and ends with laggards. As innovators have the highest risk-taking attitude, they will be the first to adopt a new technology. The subsequent groups, like early adopters, early majority, late majority, and laggards, will assume the same technology in sequence as they have decreasing risk acceptance capability. Besides, technology adoption experience by formers propagates information, lowering the perceived risk. However, such a risk-based model fails to interpret how people adopt high-tech innovations.
Genesis of Rogers Innovation Diffusion Theory
Hybrid seed adoption decision by different target groups underpins Rogers innovation diffusion theory. Dr. Rogers analyzed farmers and their reaction to a new type of corn seed. Despite the proven higher yield in experimental test farming under the supervision of agricultural scientists, farmers perceived significant risks in adopting it in actual life farming practices. As the failure of seed would lead to no crop for farmers to harvest, resulting in devastating economic results, the risk was very high. Due to varying risk absorption or handling capacity, not all the farmers adopted the seed simultaneously. Instead, there was a sequential response from different groups due to varying risk aversion and absorption capacity. Based on such observation, Rogers segmented target adopters into five groups and arranged them sequentially based on time-varying adoption responses. These five groups are:
- innovators—young, educated, fortune seeker, risk taker, etc.
- early adopters—educated opinion leaders, etc.
- early majority—middle age, experienced, pragmatists, etc.
- late majority—skeptical to change, demand proven results, etc.
- laggard—older, less educated, backward, and highly pessimistic
Rogers’ modeling of time-varying responses of different adopter groups, forming a bell-shaped curve, is based on risk or uncertainty about the functioning of technology and the economic implications caused by the failure. However, if technology does not show uncertainty in its function and performance, will it spread through the adopter groups immediately? Of course, the probability of failures and economic loss due to failures have implications for adopting new ideas. But is the risk the sole reason affecting innovation diffusion? Rogers adopter segmentation and speed of diffusion also considered proximity and cultural barriers.
Questioning the efficacy of Rogers innovation diffusion theory
How has perceived risk affected the diffusion of technology ideas like personal computers, aviation, CAT scan, and mobile phones? Despite high loss due to the failure of CAT, why has it taken less time than mobile phones to diffuse among 80 percent of the US population?
On the other hand, despite a very low probability of failure and little or negligible financial implication of failure, why did the mobile phone take almost 20 years to diffuse among 80 percent of the US population? Besides, why did mobile phones diffuse at a faster rate in less developed countries than their wealthy counterpart? Does it mean that people in less developed countries were more knowledgeable about the technology and more receptive to emerging opportunities than people of advanced ones, making them early adopters of mobile phones?
Besides, the mobile phone was invented in developed countries, and initial diffusion in the 1980s started among a small group of users in those countries. However, in the 1990s, its diffusion in less developed countries was far faster than in inventing advanced countries. Does it also mean that people in less developed countries have a very high cultural similarity and experience low distance gap with innovators of mobile phones?
Defining High-tech
For further clarity, let’s define high-tech innovation. According to a dictionary definition, high-tech refers to advanced or high technology like electronics. Within this context, let’s define high-tech as technologies that emerge in a primitive form and grow with the Flow of Ideas, resulting in increasing quality and reducing cost. For example, the mobile phone is a high-tech product. It emerged as a 3-lb machine in 1984, costing $3995 and offering only the voice option. Since then, it has been evolving, offering better quality at a decreasing cost. The underlying reason has been that the technology core has been advancing.
Similarly, in 2007, Sony introduced a 13” OLED television at a price tag of $2500; however, far better OLED TV now costs less. High-tech innovation tends to be more suitable for Getting jobs done at a decreasing cost, due to technology progression. Hence, unlike hybrid seed, economic attractiveness from high-tech innovations keeps changing.
Jobs to be done and economic benefits highly affect technology adoption
People adopt technology innovations to get their jobs done. It’s also important to note that the economic benefits of different customer groups getting the same job are unequal. Besides, the fitness and cost of high-tech innovation in getting jobs done keep changing along with the technology life cycle.
Let’s get a lesson from mobile phone adoption and the underlying reasons. The launch of Motorola Dynatec in 1984 was a significant milestone for the diffusion of mobile phone innovation. This 3lb machine costing $3995 found appeal only among a small group of busy professionals as a car phone. The underlying case was not about the perceived risk. Instead, the economic benefits of having communication while on the move and the suitability of use as a mobile phone were the significant causes.
Within 15 years of its launch, mobile phones became portable, in the true sense, due to decreasing size and weight; the cost of the handset and talk time came down significantly. As a result, it became suitable and economically attractive to a large number of users. It’s worth noting that its adoption did not rapidly increase due to the quick reduction of the likelihood of failure and consequential economic loss. It also did not happen due to the communication benefits of mobile phone usage. It also did not occur due to the proximity of a large body of user groups of less developed countries with the innovator adopter group of advanced countries. If it had remained as a 3lb, $3995 device, diffusion through a far larger group could never have been possible.
Salient attributes of high-tech innovation diffusion
Here are salient observations from mobile phone and other high-tech innovation diffusion:
- unlike hybrid seeds, the risk of failure of functioning of innovations has little role to play in the diffusion of high technology.
- instead of risk perception, the adoption decision of high-tech innovation has been due to suitability in getting jobs done and deriving economic benefits.
- the reason for the early adoption of high-tech innovation, in the premature or early stage, has been the economic benefits specific groups of users derive from unique purposes and high economic value.
- in serving the same purpose with high-tech innovations, like mobile phones, not all adopters derive the same economic benefit.
- due to the advancement of the technology life cycle, a growing number of customers find high-tech innovations economically rewarding, resulting in a time-dependent diffusion function.
Therefore, the risk perception-based Rogers innovation diffusion model can hardly explain how high-tech innovation diffuses as a time function through a growing number of customers. Besides, such reality also raises the question about the validity of segmenting customers into five groups, as suggested by Rogers.
Technology progression drives innovation diffusion
Technological progression makes high-tech innovations increasingly suitable for getting jobs done and lowering costs for a growing number of people. Due to this effect, instead of varying risk perception, high-tech innovation diffusion becomes a continuous time function. However, if technology poses or stops improving, high-tech innovation also halts diffusing further. Besides, high-tech innovation also suffers from the hype cycle. Perhaps the hype cycle and/or pause of technology progression are the underlying cause of the chasm, which has been perceived (may be wrongly) as a risk perception-based social and communication phenomenon.
For almost 50 years, Rogers’ innovation diffusion book has remained the dominant source of model about how innovations diffuse. It was developed to articulate sequential responses by different adopter groups to innovation like the hybrid seed, which suffered from the risk of failure, resulting in devastating financial consequences. Hence, varying risk perception and absorption capacity played a vital reasoning role in segmenting customer groups and developing a bell-shaped adoption model in support of innovation diffusion as a continuous time function.
However, unlike hybrid seed or similar ideas, high-tech innovations are not equally suitable for getting target jobs done by the intended customers. Besides, not all users derive the same economic benefits by adopting high-tech innovation. Moreover, unlike hybrid seeds, high-tech innovations keep growing, making them increasingly more suitable to different groups at a decreasing cost. Instead, the risk of failure to get the job done, suitability, and economic benefit are the primary reasons for time-varying responses by target customers to high-tech innovations.
Hence, Rogers innovation diffusion model, developed based on varying risk perception, appears highly inappropriate in explaining or comprehending how high-tech innovations diffuse. Due to such weakness, innovators may be left with either a wrong theory or no framework to comprehend high-tech innovation adoption patterns.
