1. two primary components: 1) a physical component which

1.         
Introduction

Life cycle is a process of maturation from innovation/birth to the
declination/death of substance. This paper is about life cycle of technology,
perhaps technology always comes out in the form of product. In this case
technology and product both can be considered as a side of coin, consequently
in a total product life it can consist of many different technologies and
similarly from innovation to declination of technology, it may be integrated
with different kind of products. Technology and product are two different term,
so both have their own life cycles. The product life cycle is based on total
product sales or market performance in a lifespan and technological life cycle
is an analysis or forecast of number of research and development projects/products
using that technology in an outlined period.

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Technological life cycle can be also explained as a measure of flow of
technology in market population. It is taken into consideration before
launching any product to the market, for existing product most of the
industries verify the stage of current technology in technological life cycle
while in the case of new product, it is always preferred to cop up the product
technology with the current trend.

Some important terminology: –

1.    
Technology:
-The term technology has been given various definitions by different
literatures. According to Kumar et. al (1999) technology consists of two
primary components: 1) a physical component which comprises of items such as
products, tooling, equipments, blueprints, techniques, and processes; and 2)
the informational component which consists of know-how in management,
marketing, production, quality control, reliability, skilled labor and
functional areas. Thus, technology is depended on different products,
principles or other technologies.

 

2.    
Diffusion:
– ”diffusion as the process by which an innovation is communicated through
certain channels over time among the members of a social system” 1

 

Diffusion process is always the important part of technological life
cycle, the way in which innovation/product get spread is called diffusion of
innovation; The main outcome of any model of technological life cycle is the
diffusion process. In the history of Innovation and technology diffusion, The
concept was first studied by the French sociologist Gabriel Tarde (1890) and by
German and Austrian anthropologists such as Friedrich Ratzel and Leo Frobenius.2 Its basic epidemiological or internal-influence form
was formulated by H. Earl Pemberton, who provided examples of institutional
diffusion such as postage stamp 2.

After the Gabriel Tarde (1890), there were many researchers, who have
discovered different models of innovation and technology lifecycle and
product/technology adaptation process These models are mostly related with the relative
speed of diffusion and how the adoption process works. Every author or
researcher has given their analysis in the form of models, graphs or equations,
in their domain, for the relative audience. Most of the models are derived by
scholars to analyse or forecast the technology diffusion in predefined area of
research, which are generally not be implemented on different area of
application.

Everett Rogers has divided such diffusion research tradition in 10
divisions (Diffusion of innovation, 3rd ed., p.44-45), which are
Anthropology, Early sociology, Rural sociology, Education, public health and
medical sociology, Communication, marketing, geography, general sociology and
other tradition.

Here I have listed out some of very influential studies, Gabriel Tarde (1903);
Mansfield (1961); Roger (1965); Hype cycle (1995).

 

2.         
Description
of different model of technological life cycle

2.1          
Tarde Gabriel:-  The Laws of Imitation

Gabriel Tarde was a French sociologist, social psychologist, and
criminologist born in 1843, was a French judge (1869-1894) and a professor of
modern philosophy (1990) at Collège de France. In the field technology
diffusion and adoption Trade had observed universal phenomenon of repetition.

Trade was one of the oldest researchers, who suggested
S shaped curve for innovation diffusion, he mentioned that technology adoption
is very less in early stage and increases with time, which tends to stable in
the final stage and form a S-shape curve. For example, Tarde (1969, pp.
29-30) observed that an innovation is first adopted by an individual who is
socially closest to the source of the new idea, and that it then spreads gradually
from higher-status to lower-status individuals. Further, Tarde (1969, p. 27)
proposed as one of his most fundamental “laws of imitation” that the
more similar an innovation is to those ideas that have already been accepted.

To Gabriel Tarde, the diffusion of innovations was a
basic and fundamental explanation of human behavior change: “Invention and
imitation are, as we know, the elementary social acts” (Tarde, 1969, p.
178). Thus, Trade had given the direction to technology diffusion towards
S-shaped curve, this was further analyzed by many scholars based on their field
of market and customer, which leads to different analytical equations and
different slope of s-curve.

2.2          
Mansfield model

Ed Mansfield had analyzed logistic diffusion for the many years. He has
also given the S-shaped curve in the analysis of diffusion of logistic
technology. Mansfield’s work is related to many studies of evolutionary
economists, however the logistic law, the logistic process and the logistic
curve are characteristic signatures of competitive selection processes in the
presence of economic variation 3.

In one of the twelve studies (12 innovation studies in
4 sectors) Mansfield reports that of 30 randomly chosen railroads over 70% took
more than 8 years to fully adopt the innovation while 10% took more than 14
years. From this and another similar results Mansfield derived two conclusions.
”First, the diffusion of a new technique is generally a rather slow process.
Second, the rate of imitation varies widely.” 4

Mansfield
has given his deterministic model (Mansfield,1961) in two stages. In the first
stage He assume that the proportion of “hold-outs” at time t that
introduce the innovation by time t. t+1 is a function of four variables, (1)
the proportion of firms that already introduced it by time t, (2) the
profitability of installing it, (3) the size of the investment required to
install it, and (4) other unspecified variables. 4

……………………………………. (1)

Where,
?ij(t) be
the proportion of “hold-outs” (firms not using this innovation) at
time t that introduce it by time t +1, nij be the total number of
firms on which jth innovation in the ith industry are based
( j= 1,2,3;i = 1,2,3,4). mij be the number of these firms having
introduced this innovation at time t, ?ij be the profitability of
installing this innovation relative to that of alternative investments, and Sij
be the investment required to install this innovation as a per cent of the average
total assets of these firms.4

 

After a
series of manipulations and approximations, he transformed above function into
a usable expression as below. Where, lij is integration constant; Øijt
is the rate of imitation.

.…………………..…….. (2)

Thus,
the growth over time in the number of firms having introduced an innovation should
conform to a logistic function, it can be shown that the rate of imitation is
governed by only one parameter- Øijt. Assuming that the sum of the
unspecified terms in uncorrelated with ?ij and Sij and
that it can be treated as a random error term. 4

.……………..…….. (3)

Where,
bi equals a12 plus the expected value of this sum and zij
is a random variable with zero expected value. Hence, the expected value of Øij
in a particular industry is a linear function of ?ij and Sij. 4

Encapsulating
the model analysis, two predictions can be made. First, the number of firms
having introduced an innovation, if plotted against time, should approximate a
logistic function. Second, the rate of imitation in a particular industry
should be higher for more profitable innovations and innovations requiring
relatively small investments. More precisely, Øij, a measure of the
rate of imitation, should be linearly related to ?ij and Sij..
4

2.3          
Rogers’ technology adoption
life cycle

Everett
Rogers has researched deeply on how, why and at which rate diffusion process
occurs. Main points covered in Rogers study are characteristic of innovation
which influence adoption, decision making process of adopter, consequences of
adoption and innovation and communication channel.

As
per Rogers, there
is a specific way in which the time dimension is involved in the diffusion of
innovations. The
rate of adoption is usually measured by the length of time required for a
certain percentage of the members of a system to adopt an innovation.
(Rogers,1983, p.p-23)

Based
on the time taken by individual to adopt specific technology, they are
classified in different category. which are,

Figure 1: –
Adopter categorization based on innovativeness (Rogers,1983)

 

(1)  
Innovators: – Innovators are eager to try new ideas. Usually,
innovators have substantial financial resources, and the ability to understand
and apply complex technical knowledge. Point of interest of every innovators
are mostly similar, but they may be from different geographical areas. The salient value of the innovator is venturesomeness. The innovator
must also be willing to accept an occasional setback when one of the new ideas
he or she adopts proves unsuccessful, as inevitably happens. (Rogers,1983) Innovators
are just 2.5% of adopters. However, Innovators are the most important part of
diffusion process because they are the way to launch new ideas/products/technologies

(2)  
Early Adopters: – Early adopters are localities, they are more integrated part of the
local social system than are innovators. they serve as a role model (opinion
leaders) for many in a social system. Early adopters are almost 13.5% of the
adopters. ”The early adopter is respected by his peers and is the embodiment
of successful and discrete use of new ideas. So, the
role of the early adopter is to decrease uncertainty about a new idea by
adopting it, and to pace the diffusion process by spreading it to their
networks.”(Rogers,1983)

Rogers has generalized the Characteristics
of early adopters by 31 generalizations in his book Diffusion of innovation.

(3)  
Early Majority: –  They
take more time to adopt new ideas in comparison with innovators and early
adopters. ”The early majority interact frequently with their peers, but seldom
hold leadership positions.” Members of the early majority
category will adopt new ideas just before the average member of a social system. The early majority’s unique position between the very early and the
relatively late to adopt makes them an important link in the diffusion process.
(Rogers,1983)

(4)  
Late Majority: – The late majority adopt new ideas just after the average member of a social
system. Late majority adopt the change after change of major public of social
system, they are not the last, but they adopt new innovation after successful
adoption of almost all. So, they have least opinion leadership among all above.

(5)  
Laggards: – Laggards are the last group who adopts new idea. They have no
leadership, they adopt innovation at the time when it is about to disappear,
and opinion leaders have already replaced it with another innovation. As per
rogers this type of people is 16% of total adopters.

It is acceptable
that every person in the market is not the adopter, but the process of becoming
adopter is a significant decision. Rogers argument says every individual have
their own decision-making process, this process can be described in five stages.
(Rogers,1983)

 (1)
knowledge—Knowledge of use and function of technology or product to a
individual or group. (2) persuasion—It forms a favorable or unfavorable
attitude toward the innovation; (3) decision—Individual or group activities
that lead to a choice to adopt or reject the innovation; (4) implementation—Adopters
put an innovation into use; and (5) confirmation—One the individual decides to
adopt or reject the innovation, it may change due to conflicting messages about
the innovation. Thus, confirmation is necessary 

As shown in above figure, this decision process may
take 2-3 years, for innovators this time is shorter (self-motivated/ eagerness
to use new ideas) while for laggards this time is more than 3 years (Stationary
mindset) . Decision making time can be calculated as time taken to reach the stage-3
(decision) or in some case stage-5 (confirmation)

 

2.4          
Gartner hype cycle

Gartner hype cycle shows process of introducing new
product in the market. How a company can manage product deployment to achieve
certain goal. Gartner hype cycle is named as this hype cycle was researched by
the IT firm Gartner Inc. Jackie Fenn, the author of the book and originator of
the hype cycle model, had been working on the analysis of emerging technologies
in the IT industry at Gartner Inc

As shown in Figure 3, the “Hype cycle” shows
expectation, and its varying factors with respect to time. Specifically, it
shows that there is a hike of expectation and inversely a sudden slip due to
the exaggeration of expectation in the very early stages of the diffusion. But,
by the maturity to some extent, market expectation begins to diminish.

As per the Gartner inc, Each Hype Cycle distinguished into the five key phases
of a technology’s life cycle as shown in fig.3.

(1)  
Technology trigger: – First stage
shows the people begin accepting the innovation and word get hike quickly.
Market gives hike and start the illusory expectation, based on the products
features and improvability, commercialization or market value it gets more or
less hike.

(2)  
Peak of inflated expectation: -This
phase starts before the peak of technology advancement, where further
improvement is very hard, after certain change advancement in innovation is not
possible or time taking but, the market still have unrealistic expectation and
it leads to decline of product market.

(3)  
Trough of disillusionment: – This
stage begins with the sharp fall, where research for advancement fails and
customers and company forecast the end of product, but some change may hit the
advantage of product and can be again rise in the market.

(4)  
Slope of enlightenment: – This phase
is rise of product after declination when adopters recognizes the product
effectiveness and start using it predominantly. Such rise after fall shows
products enlightenment, where public accept the product widely.  

(5)  
Plateau of productivity: -After
getting starting force in market (in above stage) product’s scope increases
during this phase. In this time product gets long term business.

As per the authors of hype cycle 5, hype cycle is not limited to
single product range or sector unlike other technological life cycle models. It
can be applicable for in many products. Each year Gartner inc. publishes a hype
cycle curve for trending technologies since 2008, They claim that this
phenomenon is not a new, but it repeats itself with each innovation. ”Hype
cycle curve pattern occurred with canals and railroads in the 1700s and 1800s;
the telephone in the late nineteenth and early twentieth centuries; automobiles
and radio in the early decades of the twentieth century; the jet engine,
rockets, and atomic energy in the 1950s and ’60s; the Internet in the 1990s;
and most recently biotechnology and nanotechnology.” 5

To select the right innovation at the right time, developers have
suggested STREET process. ”It is focused on the period in which a decision is
made to adopt innovation until a ‘transfer’ stage where innovation becomes
widely accepted and embraced in the society.”7.The street process is divided
into six stages. Scope, Track, Rank, Evaluate, Evangelize, Transfer. Most
important thing to notice is the STREET process gives a decision asoutput not a
product or innovation. Each step of this process is discussed on detail by
authors of  Mastering the Hype Cycle: How to Choose the Right
Innovation at the Right Time and

3.         
Comparison
of different models of technological life cycle.

Comparison of different models
is a complex task, As discussed earlier rogers has divided different models in ten
types. Which can be because of uncertainty of shape of technology diffusion
curve in diffusion process of different product in different market (Geographical
position) and in different condition of marketing (accepted or imposed).
Consequently, to compare all models together with each other will not be
correct way of comparison. But above discussed models are some of the most
common models of technological life cycle which are used as a generalized
instead of specific technological or sector.

From the above-mentioned
models, Gabrial trade had given theoretical aspect about how society accepts
the innovation and how it is depended on the different types of human thought process.
He has given an important terminology named rate of imitation which is further
discussed by rogers as rate of adoption. Trade’s theories are the basic
phenomena for Rogers’ and Gartner Hype cycle. Mansfield’s deterministic model
also discuss about rate of imitation but in terms of empirical relations and
analytical calculation.

The outcome of Mansfield’s
work is approximate profitability, rate of imitation and when and how-much to
invest in an innovation. Similarly, hype cycle also helps in investment timings
for an innovation, Mansfield’s models take diffusion not only as a adoption of
technology by a consumer but acceptation of innovation by other industries.
This model is not directly comparable to reogers’ but it can be comparable to
hype cycle for some results. While, Rogers’ technology adoption cycle is more
concentrated on adopters and human nature, where adopters are the consumers. Gartner
inc. the firm, publishes hype cycle curve for trending technology and
innovations in each year. This hype cycle curve also reveals the profitability
and adoption data. So, by making list of data of outcome it is possible to
compare hype cycle with Manfield’s model.

Rogers generalized technological
model and hype cycle both have similar function, both are used to approximate
ups and down of technology in market, in which both leads to result by
considering different theoretical aspects. Both model discuss about how
technology spreads in market and what is the human thought process unto
adopting innovation.

As per rogers’ bell-shaped diffusion
curve takes place based on the adopter and their thought process
(decision-making process); parameters affecting adopter’s decision process are
described as a attributes of innovation. When innovation has such
characteristics then it can be spread quickly in market. Those attributes are,
(1) Relative advantage, (2) compatibility, (3) complexity, (4) trialability,
and (5) observability. Relation between rate of adoption and relative advantage
is very influential (Positively), this relation is shown in detailed by rogers
in book diffusion of
innovation(Table-6.1). Compatible is discussed as ”An
innovation can be compatible or incompatible (1) with sociocultural values and
beliefs, (2) with previously introduced ideas, or (3) with client needs for innovations
” 1

Rogers has given generalization for complexity and tribality as they are
inversely and directly proportional to rate of adoption respectively.

On the other hand, Gartner hype cycle is more concentrated on product
launching and adopting phenomena majorly from the manufacturer or by company’s
point of view. ”The hype cycle takes into account
customers’ emotional responses while the existing cycle models, which are based
on a theoretical and idealistic approach, assume that customers make logical
and rational decisions in the market.”7. However, hype cycle also explains some traps to
adopters for adoption of innovation which are (1) adopting too early, (2) giving
up too soon, (3) adopting too late or hanging on too long. Selecting innovation
by considering this adoption situation adopter allows to get maximum advantage
of innovation.

Summing up
all the comparison, it can be concluded that all the models of technological
life cycle are based on S-shaped curve, all models have different
terminologies, calculations or logics to define their own points; these
difference in models may be happens due to base of model in particular
technological field. ( Manfield’s- limited to four industrial sectors; Rogers –
most research examples are in the field of rural sociology ; Hype cycle – main
research is in IT (Information technology)).However, human nature towards
adoption of innovation is noticed to be similar in examples of all models. But
the adoption rate or imitation rate can be different,this phenomena is deeply
discussed and applied in hype cycle as a 
speed of hype cycle in 8. Research behind different theories of
diffusion of innovation or the technological life cycle is done by many scholars,
but Everett Rogers have done the clearest comparison by claiming, data
gathering and organizing these disparate cases in Diffusion of innovation. This book enlightens major area of
innovation and technological life cycle. 8

4.         
References

 

1 Rogers, E.M.(1983), Diffusion
of innovations, .The Free Press

2 C. merle Crawford, C.A.Benedetto, Reviews CTI (2016). New Products Management: Business, Marketing.
Cram101, (Chapter 1)

3 J. S. Metcalfe (2005). Ed Mansfield and the Diffusion
of Innovation: An Evolutionary Connection. Journal
of Technology Transfer, 30 1/2, 171–181.

4 Edwin Mansfield (Oct., 1961), Technical Change and the Rate of
Imitation, Econometrica, Vol. 29, No.
4., 741-766.

5 J. Fenn and M. Raskino (2008). Mastering the Hype Cycle: How to Choose the Right
Innovation at the Right Time.USA:
Harvard business Press

6 Lajoie, EW, Bridges L (2014) Innovation decisions: Using the Gartner
Hype Cycle. Library Leadership &
Management, 28(4)

7Article on Mastering the Hype Cycle: How to Choose the Right
Innovation at the Right Time, retrieved on 10.12.2017 from https://www.google.de/url?sa=t=j==s=web=3=0ahUKEwjkmrnk_f_XAhXFVRQKHdvnCxAQFgg5MAI=http%3A%2F%2Fwww.arpjournal.org%2Fdownload%2Fusr_downloadFile.do%3FrequestedFile%3DARP3(1)_102-107.pdf%26path%3Dthesis%26tp%3Disdwn%26seq%3D74=AOvVaw2mP8G-Rab-qmFRIt6PHeb8j

8 J. Fenn, M. Raskino (19 July 2011). Understanding
Gartner’s Hype Cycles – 2011.
Gartner document G00214001.

9 G. Trade (1903) The Laws of Imitation, New York : Henry holt and company
(Translated from second French edition by 
E.C.Parsons )