Hochschule perhaps technology always comes out in the form

 

 

 

 

 

Hochschule Düsseldorf(HSD)

 

 

            

 

 

Comparison of Different Model of Technological
Lifecycle

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Term paper for “Innovation and Technology management”

Summer semester – 2017

Lecturer: – Prof.Dr. Carsten Decert

 

 

 

 

 

 

 

Anand Parikh  

M.Sc.
Mechanical Engineering             

Matriculation No. – 753156

v  Content
?    Content ii
1.    Introduction. 1
2.    Description of
different model of technological life cycle. 2
2.1     Tarde Gabriel:
–  The Laws of Imitation. 2
2.2     Mansfield
model 3
2.3     Rogers’
technology adoption life cycle. 4
2.4     Gartner hype
cycle. 7
3.    Comparison of
different models – The Conclusion. 9
4.    References. 11
 

 

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.

     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. If 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.-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 hidden 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.” 1 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 an
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.

Figure 2: – Adopter’s
decision process (Rogers,1983)

As shown in 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.

 

Figure 3:
– Hype cycle July – 2011 8

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 has 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 as output 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.

3.         
Comparison
of different models – The Conclusion

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 an adoption of technology by a consumer but acceptation of innovation
by other industries. This model is not directly comparable to Rogers’, 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 Mansfield’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 an attribute 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 considers 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. (Mansfield’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 phenomenon 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.

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)

10
V Kumar, U Kumar and A Persaud. (1999) Building Technological Capability
Through Importing Technology: The Case of Indonesian Manufacturing Industry. Journal
of Technology Transfer 24:81-96.