Hochschule perhaps technology always comes out in the form

     Hochschule Düsseldorf(HSD)                 Comparison of Different Model of TechnologicalLifecycle    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.

Best services for writing your paper according to Trustpilot

Premium Partner
From $18.00 per page
4,8 / 5
4,80
Writers Experience
4,80
Delivery
4,90
Support
4,70
Price
Recommended Service
From $13.90 per page
4,6 / 5
4,70
Writers Experience
4,70
Delivery
4,60
Support
4,60
Price
From $20.00 per page
4,5 / 5
4,80
Writers Experience
4,50
Delivery
4,40
Support
4,10
Price
* All Partners were chosen among 50+ writing services by our Customer Satisfaction Team

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 frominnovation/birth to the declination/death of substance. This paper is about lifecycle of technology, perhaps technology always comes out in the form ofproduct. In this case technology and product both can be considered as a sideof coin, consequently in a total product life it can consist of many differenttechnologies and similarly from innovation to declination of technology, it maybe integrated with different kind of products. Technology and product are twodifferent term, so both have their own life cycles. The product life cycle isbased on total product sales or market performance in a lifespan andtechnological life cycle is an analysis or forecast of number of research anddevelopment projects/products using that technology in an outlined period.

     Technological life cycle can be alsoexplained as a measure of flow of technology in market population. It is takeninto consideration before launching any product to the market, for existingproduct most of the industries verify the stage of current technology intechnological life cycle while in the case of new product, it is alwayspreferred to cop up the product technology with the current trend. Some important terminology: -1.    Technology:-The term technology has been given various definitions by differentliteratures. According to Kumar et. al (1999) technology consists of twoprimary components: 1) a physical component which comprises of items such asproducts, 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 andfunctional areas. Thus, technology is depended on different products,principles or other technologies.

2.     Diffusion: – ”diffusionas the process by which an innovation is communicated through certain channelsover time among the members of a social system” 1Diffusion process is always the important part oftechnological life cycle, the way in which innovation/product get spread iscalled diffusion of innovation; The main outcome of any model of technologicallife cycle is the diffusion process. In the history of Innovation and technologydiffusion, the concept was first studied by the French sociologist GabrielTarde (1890) and by German and Austrian anthropologists such as FriedrichRatzel and Leo Frobenius. 2 Its basic epidemiologicalor internal-influence form was formulated by H. Earl Pemberton, who providedexamples of institutional diffusion such as postage stamp 2.      After theGabriel Tarde (1890), there were many researchers, who have discovereddifferent models of innovation and technology lifecycle and product/technologyadaptation process These models are mostly related with the relative speed ofdiffusion and how the adoption process works. Every author or researcher hasgiven their analysis in the form of models, graphs or equations, in theirdomain, for the relative audience.

Most of the models are derived by scholarsto analyse or forecast the technology diffusion in predefined area of research,which are generally not be implemented on different area of application.     Everett Rogershas divided such diffusion research tradition in 10 divisions (Diffusion ofinnovation, 3rd ed., p.

44-45), which are Anthropology, Earlysociology, Rural sociology, Education, public health and medical sociology,Communication, marketing, geography, general sociology and other tradition.     Here I have listed out some of veryinfluential studies, Gabriel Tarde (1903); Mansfield (1961); Roger (1965); Hypecycle (1995).2.         Descriptionof different model of technological life cycle2.1          Tarde Gabriel: –  The Laws of ImitationGabrielTarde was a French sociologist, social psychologist, and criminologist born in1843, was a French judge (1869-1894) and a professor of modern philosophy(1990) at Collège de France. In the field technology diffusion and adoptionTrade had observed universal phenomenon of repetition.     Trade was one of the oldest researchers,who suggested S shaped curve for innovation diffusion, he mentioned thattechnology adoption is very less in early stage and increases with time, which tendsto 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 issocially closest to the source of the new idea, and that it then spreads graduallyfrom higher-status to lower-status individuals. Further, Tarde (1969, p.

27)proposed as one of his most fundamental “laws of imitation” that themore similar an innovation is to those ideas that have already been accepted.     To Gabriel Tarde, the diffusion ofinnovations 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 technologydiffusion towards S-shaped curve, this was further analyzed by many scholarsbased on their field of market and customer, which leads to differentanalytical equations and different slope of s-curve.2.2          Mansfield modelEd Mansfieldhad analyzed logistic diffusion for the many years. He has also given theS-shaped curve in the analysis of diffusion of logistic technology.

Mansfield’swork is related to many studies of evolutionary economists, however thelogistic law, the logistic process and the logistic curve are characteristic signaturesof competitive selection processes in the presence of economic variation 3.      In one of the twelve studies (12 innovationstudies in 4 sectors) Mansfield reports that of 30 randomly chosen railroads over70% took more than 8 years to fully adopt the innovation while 10% took morethan 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     Mansfieldhas given his deterministic model (Mansfield,1961) in two stages. In the firststage He assume that the proportion of “hold-outs” at time t thatintroduce 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) theprofitability of installing it, (3) the size of the investment required toinstall it, and (4) other unspecified variables.

4 ……………………………………. (1)Where, ?ij(t) be theproportion of “hold-outs” (firms not using this innovation) at time tthat introduce it by time t +1, nij be the total number of firms on whichjth 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 thisinnovation at time t, ?ij be the profitability of installing thisinnovation relative to that of alternative investments, and Sij bethe investment required to install this innovation as a per cent of the averagetotal assets of these firms. 4     After aseries of manipulations and approximations, he transformed above function intoa usable expression as below. Where, lij is integration constant; Øijtis the rate of imitation.

.…………………..…….. (2)     Thus, thegrowth over time in the number of firms having introduced an innovation shouldconform to a logistic function, it can be shown that the rate of imitation isgoverned by only one parameter- Øijt. If the sum of the unspecifiedterms in uncorrelated with ?ij and Sij and that it can betreated as a random error term.

4 .……………..…….. (3)     Where, biequals a12 plus the expected value of this sum and zij isa random variable with zero expected value.

Hence, the expected value of Øijin a particular industry is a linear function of ?ij and Sij. 4     Encapsulatingthe model analysis, two predictions can be made. First, the number of firmshaving introduced an innovation, if plotted against time, should approximate alogistic function. Second, the rate of imitation in a particular industryshould be higher for more profitable innovations and innovations requiringrelatively small investments. More precisely, Øij, a measure of therate of imitation, should be linearly related to ?ij and Sij.

42.3          Rogers’ technology adoptionlife cycleEverettRogers has researched deeply on how, why and at which rate diffusion processoccurs. Main points covered in Rogers study are characteristic of innovationwhich influence adoption, decision making process of adopter, consequences ofadoption and innovation and communication channel.     As per Rogers, there is a specific way in which the time dimension isinvolved in the diffusion of innovations.

The rate of adoption is usuallymeasured by the length of time required for a certain percentage of the membersof a system to adopt an innovation. (Rogers,1983, p.-23)     Based on the time taken by individual toadopt specific technology, they are classified in different category. whichare, Figure 1: – Adopter categorization basedon innovativeness (Rogers,1983)(1)  Innovators:- Innovators are eager to try new ideas. Usually, innovators have substantialfinancial resources, and the ability to understand and apply complex technicalknowledge.

Point of interest of every innovators are mostly similar, but theymay be from different geographical areas. The hidden value of the innovator isventuresomeness. The innovator must also be willing to accept an occasionalsetback when one of the new ideas he or she adopts proves unsuccessful, asinevitably happens. (Rogers,1983) Innovators are just 2.5% of adopters.

However,Innovators are the most important part of diffusion process because they arethe way to launch new ideas/products/technologies (2)  EarlyAdopters: – Early adopters are localities, they are moreintegrated part of the local social system than are innovators. they serve as arole model (opinion leaders) for many in a social system. Early adopters arealmost 13.5% of the adopters. ”The early adopter is respected by his peers andis the embodiment of successful and discrete use of new ideas. So, the role ofthe 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 Characteristicsof early adopters by 31 generalizations in his book Diffusion of innovation.

(3)  EarlyMajority: –  They takemore time to adopt new ideas in comparison with innovators and early adopters. ”Theearly majority interact frequently with their peers, but seldom hold leadershippositions.” 1 Members of the early majority category will adopt new ideasjust before the average member of a social system.

The early majority’s uniqueposition between the very early and the relatively late to adopt makes them animportant link in the diffusion process. (Rogers,1983)(4)  LateMajority: – The late majority adopt new ideas just after theaverage member of a social system. Late majority adopt the change after changeof major public of social system, they are not the last, but they adopt newinnovation after successful adoption of almost all. So, they have least opinionleadership among all above.(5)   Laggards: – Laggardsare the last group who adopts new idea. They have no leadership, they adoptinnovation at the time when it is about to disappear, and opinion leaders havealready replaced it with another innovation.

As per rogers this type of people is16% of total adopters.      It is acceptable that every person in themarket is not the adopter, but the process of becoming adopter is a significantdecision. Rogers argument says every individual have their own decision-makingprocess, this process can be described in five stages. (Rogers,1983) (1)knowledge—Knowledge of use and function of technology or product to anindividual or group. (2) persuasion—It forms a favorable or unfavorableattitude toward the innovation; (3) decision—Individual or group activitiesthat lead to a choice to adopt or reject the innovation; (4)implementation—Adopters put an innovation into use; and (5) confirmation—Onethe individual decides to adopt or reject the innovation, it may change due toconflicting 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 ismore than 3 years (Stationary mindset). Decision making time can be calculatedas time taken to reach the stage-3 (decision) or in some case stage-5(confirmation) 2.4          Gartner hype cycleGartnerhype cycle shows process of introducing new product in the market.

How acompany can manage product deployment to achieve certain goal. Gartner hypecycle is named as this hype cycle was researched by the IT firm Gartner Inc. JackieFenn, the author of the book and originator of the hype cycle model, had beenworking on the analysis of emerging technologies in the IT industry at GartnerInc.     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 tothe 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 8As per the Gartner inc, Each Hype Cycle distinguished into the five key phasesof a technology’s life cycle as shown in fig.3.(1)  Technologytrigger: – First stage shows the people begin accepting theinnovation and word get hike quickly. Market gives hike and start the illusoryexpectation, based on the products features and improvability,commercialization or market value it gets more or less hike.

(2)  Peakof inflated expectation: -This phase starts before the peak oftechnology advancement, where further improvement is very hard, after certainchange advancement in innovation is not possible or time taking but, the marketstill has unrealistic expectation and it leads to decline of product market.(3)  Troughof disillusionment: -This stage begins with the sharp fall, where research for advancement fails andcustomers and company forecast the end of product, but some change may hit theadvantage of product and can be again rise in the market.(4)  Slopeof enlightenment: – This phase is rise of product afterdeclination when adopters recognizes the product effectiveness and start usingit predominantly. Such rise after fall shows products enlightenment, wherepublic accept the product widely.   (5)   Plateau of productivity: -Aftergetting starting force in market (in above stage) product’s scope increasesduring this phase. In this time product gets long term business.

     As per theauthors of hype cycle 5, hype cycle is not limited to single product range orsector unlike other technological life cycle models. It can be applicable forin many products. Each year Gartner Inc.

publishes a hype cycle curve fortrending technologies since 2008, They claim that this phenomenon is not a new,but it repeats itself with each innovation. ”Hype cycle curve pattern occurredwith canals and railroads in the 1700s and 1800s; the telephone in the latenineteenth and early twentieth centuries; automobiles and radio in the earlydecades of the twentieth century; the jet engine, rockets, and atomic energy inthe 1950s and ’60s; the Internet in the 1990s; and most recently biotechnologyand nanotechnology.” 5      To select the right innovation at theright time, developers have suggested STREET process. ”It is focused on theperiod in which a decision is made to adopt innovation until a ‘transfer’ stagewhere innovation becomes widely accepted and embraced in the society.” 7.

Thestreet process is divided into six stages. Scope, Track, Rank, Evaluate,Evangelize, Transfer. Most important thing to notice is the STREET processgives a decision as output not a product or innovation. Each step of thisprocess is discussed on detail by authors of Mastering the Hype Cycle: How to Choose the Right Innovation at the Right Time.

3.         Comparisonof different models – The ConclusionComparison of different models is a complex task. As discussedearlier rogers has divided different models in ten types. Which can be becauseof uncertainty of shape of technology diffusion curve in diffusion process ofdifferent product in different market (Geographical position) and in differentcondition of marketing (accepted or imposed).

Consequently, to compare allmodels together with each other will not be correct way of comparison. Butabove discussed models are some of the most common models of technological lifecycle which are used as a generalized instead of specific technological or sector.     From theabove-mentioned models, Gabrial trade had given theoretical aspect about howsociety accepts the innovation and how it is depended on the different types ofhuman thought process. He has given an important terminology named rate ofimitation which is further discussed by rogers as rate of adoption. Trade’stheories are the basic phenomena for Rogers’ and Gartner Hype cycle. Mansfield’sdeterministic model also discuss about rate of imitation but in terms ofempirical relations and analytical calculation.      The outcomeof Mansfield’s work is approximate profitability, rate of imitation and whenand how-much to invest in an innovation.

Similarly, hype cycle also helps ininvestment timings for an innovation, Mansfield’s models take diffusion notonly as an adoption of technology by a consumer but acceptation of innovationby other industries. This model is not directly comparable to Rogers’, but itcan be comparable to hype cycle for some results. While, Rogers’ technologyadoption cycle is more concentrated on adopters and human nature, whereadopters are the consumers. Gartner Inc. the firm, publishes hype cycle curvefor trending technology and innovations in each year. This hype cycle curvealso reveals the profitability and adoption data. So, by making list of data ofoutcome it is possible to compare hype cycle with Mansfield’s model.      Rogers generalizedtechnological model and hype cycle both have similar function, both are used toapproximate ups and down of technology in market, in which both leads to resultby considering different theoretical aspects.

Both model discuss about howtechnology spreads in market and what is the human thought process untoadopting innovation.     As per rogers’bell-shaped diffusion curve takes place based on the adopter and their thoughtprocess (decision-making process); parameters affecting adopter’s decision processare described as an attribute of innovation. When innovation has suchcharacteristics 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 advantageis very influential (Positively), this relation is shown in detailed by rogersin book diffusion ofinnovation(Table-6.1). Compatible is discussed as ”An innovation can be compatible or incompatible (1) with socioculturalvalues and beliefs, (2) with previously introduced ideas, or (3) with clientneeds for innovations” 1. Rogers has given generalization for complexity andtribality as they are inversely and directly proportional to rate of adoptionrespectively.

     On theother hand, Gartner hype cycle is more concentrated on product launching andadopting phenomena majorly from the manufacturer or by company’s point of view. ”The hype cycle considers customers’ emotionalresponses while the existing cycle models, which are based on a theoretical andidealistic approach, assume that customers make logical and rational decisionsin the market.” 7.

However, hype cycle also explains some traps to adoptersfor adoption of innovation which are (1) adopting too early, (2) giving up toosoon, (3) adopting too late or hanging on too long. Selecting innovation byconsidering this adoption situation adopter allows to get maximum advantage ofinnovation.      Summing upall the comparison, it can be concluded that all the models of technologicallife cycle are based on S-shaped curve, all models have differentterminologies, calculations or logics to define their own points; thesedifference in models may be happens due to base of model in particulartechnological field.

(Mansfield’s- limited to four industrial sectors; Rogers –most research examples are in the field of rural sociology; Hype cycle – mainresearch is in IT (Information technology)). However, human nature towardsadoption of innovation is noticed to be similar in examples of all models. Butthe adoption rate or imitation rate can be different, this phenomenon is deeplydiscussed and applied in hype cycle as a speed of hype cycle in 8. Researchbehind different theories of diffusion of innovation or the technological lifecycle is done by many scholars, but Everett Rogers have done the clearestcomparison by claiming, data gathering and organizing these disparate cases in Diffusion of innovation.

This bookenlightens major area of innovation and technological life cycle. 4.         References1 Rogers, E.M. (1983),Diffusion of innovations, The FreePress2 C. merleCrawford, C.A. Benedetto, Reviews CTI (2016).

New Products Management: Business, Marketing. Cram101, (Chapter 1)3 J. S.

Metcalfe (2005). Ed Mansfield and the Diffusionof Innovation: An Evolutionary Connection. Journalof Technology Transfer, 30 1/2, 171–181.4 EdwinMansfield (Oct. 1961), Technical Change and the Rate of Imitation, Econometrica, Vol. 29, No. 4.

, 741-766.5 J.Fenn and M.

Raskino (2008). Mastering theHype Cycle: How to Choose the Right Innovation at the Right Time. USA: Harvard business Press6 Lajoie, EW,Bridges L (2014) Innovation decisions: Using the Gartner Hype Cycle.

Library Leadership & Management,28(4)7Article on Masteringthe Hype Cycle: How to Choose the Right Innovation at the Right Time, retrievedon 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). UnderstandingGartner’s Hype Cycles – 2011.Gartner document G00214001.

9G. Trade (1903) The Laws of Imitation,New York: Henry holt and company (Translated from second French edition by E.C.Parsons)10V Kumar, U Kumar and A Persaud. (1999) Building Technological CapabilityThrough Importing Technology: The Case of Indonesian Manufacturing Industry.

Journalof Technology Transfer 24:81-96.