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Journal of Management (JOM)

Volume 5, Issue 5, September – October 2018, pp. 97–143, Article ID: JOM_05_05_013

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A JOURNEY THROUGH THE EVOLUTION OF

THEORIES AND MODELS OF ADOPTION OF

INNOVATIONS (YEARS: 1981-1999)

Sylesh Nechully

Research Scholar, UPES, Dehradun

Dr. S.K. Pokhriyal

Professor and Head – Energy Management, School of Business – UPES, Dehradun

Dr. Saju Eapen Thomas

Dept Chair – Marketing, City University College Ajman, UAE

ABSTRACT

The study furnishes in a nut shell the evolution of diffusion theories and models

from 1981 till 1999. Various steps and factors involved are reviewed and consolidated

to give scholars a holistic insight to the process of diffusion. The practical

implications of these theories/models are also discussed as to how the

adoption/diffusion can be accelerated by manipulating the variables. Narrative

Synthesis Approach is used to draft this paper. Narrative Synthesis is all about

systematically reviewing diverse literature and concluding findings using words and

text. Four data bases – EBSCO, Petro One, JSTOR and Pro Quest were used for

Literature search. Primary Search was done using the Key words mentioned and its

various combinations to identify the relevant theories/models in a particular period

and subsequently the name of the authors/theories/models were used to identify the

core paper/book. Diffusion literature is very vast. There are umpteen numbers of

studies available. None of these models can be generalized. The direction and

intensity of impact of factors varies based on context, product, organization, industry

and country.

Keywords: Innovation; Diffusion; Adoption; Implementation; Technology; Process;

Organization

Cite this Article: Sylesh Nechully, Dr. S.K. Pokhriyal and Dr. Saju Eapen Thomas, A

Journey through the Evolution of Theories and Models of Adoption of Innovations

(Years: 1981-1999): A study of Bhopal city. Journal of Management, 5(5), 2018, pp.

97–143.

http://www.iaeme.com/JOM/issues.asp?JType=JOM&VType=5&IType=5

A Journey through the Evolution of Theories and Models of Adoption of Innovations (Years:

1981-1999)


1. INTRODUCTION

Innovation is defined as the Creation of new ideas, services and products to foster

development and thereby to enhance the quality of life in a society (UAE Ministry of Cabinet

Affairs, 2015)

Innovation and Invention are interchangeably used in today’s common parlance. In fact,

Innovation and Invention is different (Walker, 2015). Innovation is not a fancy synonym for

Invention. “There is no word as “Invention” in business dictionaries today. Innovation is the

commercialization of Invention. Invention is creating something new, which has never existed

before – a product, process or service. The process of matching Invention to Customer

Requirement is innovation. (Garud, Nayyar and Shapira, 1997). Innovation is about putting

invention to use in practical sense. Invention without practical significance is of no use in

commercial terms. Many “Successful Inventions” are “Innovation failures” (Burt, 1992).

Most of the innovations are enhancements to existing products or processes or services by

implementing a single or combination of inventions. The successful journey from “Invention”

to “Innovation” requires in-depth knowledge of consumer behavior, sufficient resources,

marketing strategies/skills and management support. (Grasty, 2012). Herbert (2016) defines

innovation as an art of manipulating an invention to sell it to the real world market.

Lopez, Torres and Gutierrez (2015) break down innovation in to four types based on

“Newness” of the Technology and the “Market” where the companies operate.

Figure 1 Types of Innovation (adapted from Lopez, Torres and Gutierrez, 2015)

Incremental Innovation (Muckersie, 2016) is the process of adding extra features to your

existing product (or even removing some features to enhance user convenience) and selling it

to the same market. Extra features increase the frequency of usage or facilitate different

applications of the existing product for various requirements. At times, removal of features

enhances customer convenience or lowers the cost. Incremental innovation focusses on

increasing the value delivered by the existing product to existing customers. 70% of the

Sylesh Nechully, Dr. S.K. Pokhriyal and Dr. Saju Eapen Thomas


innovations are incremental innovations. Architectural Innovation (Lopez, Torres and

Gutierrez, 2015) is simply introducing the existing technology/product/Service to a new or

unfamiliar market to increase customer base. Thorough market study has to be conducted

before entering new market/segment. Most often, the technology or product/service has to be

tweaked or modified to meet the customer requirements. Disruptive innovation (Christensen,

Raynor and McDonald, 2015) is about applying new technology to existing market. The

existing customers have to be educated about the latest product/technology/service. One of the

advantages is that the customers are already familiar with the brand name. “Reputation” of the

brand helps to a great extend in promoting disruptive innovations. Radical Innovation (Rouse,

2016) involves introducing technologies/products or service that revolutionizes the way

industry operates. It makes the existing industry obsolete and creates new one(s). The new

technology/product will have significant improvements over the existing ones in the market.

2. METHODOLOGY

The Researcher applies a Narrative synthesis approach to review the literature. Narrative

synthesis is a process of drawing conclusions from diverse literature using words and texts.

Narrative synthesis helps to identify complex interrelationships between various concepts

from various sources. Narrative synthesis helps to systematically review the studies already

done and add unique original views of the researcher to the existing body of knowledge. Four

data bases – EBSCO, Petro One, JSTOR and Pro Quest were used for Literature search.

Primary Search was done using the Key words mentioned and its various combinations to

identify the relevant theories/models in a particular period and subsequently the name of the

authors/theories/models were used to identify the core paper/book.

3. EVOLUTION

DiMaggio and Powell (1983) proposed “The diminishing mimetic-isomorphism theory”

to explain different types of Isomorphism. Isomorphism is a process by which the companies

in a particular market try to “copy” others to become analogous under same settings.

(DiMaggio and Powell, 1983). The process of Isomorphism is divided into three categories

(1) Coercive Isomorphism – Adoption due to legitimacy (2) Mimetic Isomorphism –

Adoption due to imitation (3) Normative Isomorphism – Adoption for want of

professionalism.

Dervin’s Sense making theory explains how customers make purchase decisions in

practical scenarios. Customer has a situation (Current state) in hand which he wants to convert

to a desirable state (Outcome) (Dervin, 1983). There exists a “Gap” between current state and

the desired outcome. A customer tries to bridge this gap by searching information which will

help him to do so.


1981-1999)


Figure 2 Dervin’s Sense Making Triangle (adapted from Dervin, 1983)

An “Innovative” product should be positioned in such a way to bridge the gap between

“Situation” and “Outcome (Wilson and Pelham, 1996). Companies should help the customers

by providing them the relevant information for closing this “Gap”

Figure 3 Wilson’s Gap Analysis (adapted from Wilson, 1996)

Dosi (1982) states that the main source of economic growth in a country is “Innovation”.

Companies learn through “Trial and Error method” or when confronted with unexpected

success. Dosi described the technical change in a particular industry or country in terms of

“Technological Paradigm” and “Technology Trajectory”. Technological Paradigm refers to a

group of radical innovations which can have a significant impact on industry as a whole in

catering to its requirements. Technological Paradigm defines the direction along which the

future innovations or technological improvements happens or direction of future RandD.

Technology is used to solve the problems faced by the Industry and the problems to be solved

are selected by the “Technological Paradigm” itself. Firms in a particular industry are

heterogeneous in terms of profitability, size and productivity.

Tolbert and Zucker (1983) studied the adoption of administrative procedures from 1880 to

1935 and formulated “The performance and then legitimacy driven theory”. An adoption in

the beginning happens due to the benefits of the innovative procedures and at a later stage

happens due to the fact that “others’ in the geographical area have adopted it. As the process

of adoption progresses, the innovative procedure becomes a “Social fact or Norm” and others

also try to adopt it for want of compliance. Adopters become legitimate once adopted. Early

adopters accept the innovative procedures for its “Performance” and late adopters for its

“legitimacy. This theory is relevant for the adoption of innovative technology or service.



Early adopters carefully evaluate a technology and accept it if and only if it is beneficial for

them. Adopters make a careful study about the work settings and competency of the personal

prior to adopting the technology. But late adopter adopt the new technology for the “sake of

adopting” it – just because it has been adopted by others in the market.

Early adopters are influenced by the process of Mimetic Isomorphism and as time

progresses the influence decreases and the influences of Coercive and Normative

Isomorphism dominates (Parent and Tingling, 2002). Early adopters uses traditional criterion

of “economic benefits” for selecting a product – if they do not have others to replicate. But as

the adoption progresses – they starts imitating others in the same sector. In the later stages of

adoption, adoption is influenced by Coercive and Normative isomorphism and the effects of

Mimetic Isomorphism decreases. Adopters adopt for want of compliance to certain standards

established by Government/Industry. As time further progresses, Coercive Isomorphism gets

converted to normative isomorphism. Usage of a particular technology or service creates an

impression of “professionalism” and companies tend to project a “professional” image.

Tornatzky (2003) stated that big companies or corporates enforce – Coercive and Normative

pressure on the dependent smaller firms to adopt a particular innovation.

Institutional Theory posits that an Organization’s decision to adopt a new technology is

not just based on the benefits it renders to an organization or economical consideration but

based on social factor compliance and desire for legitimacy (Scott and Christensen, 1995).

The environment in which an organization operates plays a significant role in decision

making. A firm in different environments reacts to same challenge in different ways. (Knetter,

1989). Martinsons (1998) states in his theory – Theory of Institutional Deficiencies that

personal contacts through networking sites, informal information available and unhealthy

government – company relationships stifles diffusion of innovation. Institutional Theory

explains how social Norms and Routines provide guidelines for consumer behavior.

Lindzey and Aronsson (1985) describes Social contagion as “Spread of behavior from one

person in the society to another, one person providing “Stimulus” or reference for imitating

the behavior or due to urge for conformity”. Social Contagion Theory explains the

“propensity to imitate the adoptive behavior of innovation” as a factor affecting the diffusion

of innovation. Adoption is triggered by the imitative action not by a rational decision

considering the beneficial aspects of innovation. Le Bon (1895) argued that information or

belief spread through a society through “unconscious” process of social contagion. Le Bon

attributes the reason of “Crowd Panic” to social contagion concept. Social contagion leads to

the spread of dangerous emotions and explains the fanatic behaviors of crowds. A Normal

person gets infected by the behavior or emotion of a fanatic for which the normal person will

regret later.

Drazena and Leonardo (1996) proposed Bandwagon Model of Innovation to explain the

adoption on innovation in a network. Drazen and Leonardo (1996) studies are based on

theories of SCT (Bandura, Ross and Ross 1961). Drazen and Leonardo (1996) posits that the

adoption in network takes place due to experimentation, observation and replication/imitation.

This model takes in to consideration competition in the market. It is assumed that the

individuals in the network have similar requirements. The individuals in the network search

for solution to their problem and if a technology is meeting the requirements, the benefits of

the technology is communicated via network channel to others .The non-adopters also

experiment with the same or similar technology and if found successful – non-adopters are

converted to adopters of the same or similar technology. The individuals in the network are

closely monitoring each other. If they find something beneficial, they will adopt it or

something similar.


1981-1999)


This model high lights the importance of “Differentiation Strategy” adoption. As per this

model – In a network, adoption follows a “Band Wagon effect” pattern because, actors adopts

a technology or a similar technology because it has benefited some other actors in the same

network

Contagion Model of Innovation (Contractor and Grant, 1996) refined Bandwagon Model

(Drazen and Leonardo, 1996) by introducing the concept of “changing environment”. The

concept is very relevant in cases of “Repeat purchases”. An individual may have adopted a

new technology due to Bandwagon effect. But as the time progresses, due to changes in his

work setting, the technology may become obsolete or irrelevant. As a result, they abandon the

existing technology and adopts the new/advanced one. Companies should always keep an eye

on changing environment and “Creative destruction” capabilities of the similar technologies.

Banerjee (1992) studied the effects of “Herd Behavior” on diffusion of innovation. Herd

behavior refers to the influence of a person on the purchasing intentions of his successor.

Purchasing in Organizations is affected by the herd behavior. While adopting an innovation,

they always make retrospection to the past – whether similar technologies are purchased in

the past by individuals or committees. Herd behavior can be seen in families as well. If father

purchases an i-phone, then the son follows suit. Many beneficial innovations are discarded

due to “Herd behavior”.

Turner and Killan (1987) explains the concept of “Collective Behavior” using

Convergence Theory. Spread of behavior is not due to “propensity to imitate” but due to

similar/same motivations which encourages individuals to behave in a similar way

collectively. Same or Similar motivation accounts for collective behavior to adopt an

innovation. “Collective Behavior reflects the attitudes, behaviors and motives of the

individuals forming the group

Boyd and Richerson (1985) states that the process of diffusion is facilitated through

“Imitation”. This study is based on “Social learning” concepts. Adoption occurs due to blind

imitation or somebody teaches them to adopt a particular behavior. Social learning is said to

have many “biases”.

Success bias – People copy from those who are perceived to be successful

Status bias – People copy from “Higher status” people

Homophily – People copy from other similar to them

Conformist bias – People adopt the technology used by most number of people

“Social Learning” plays an important role in the adoption of innovative technologies.

(Ellison and Drew, 1991). “Prospects” make a decision based on the experience of their

neighbors. In the study Ellison and Drew (1991) considered two environments – (1) the same

technology is optimal for all players (2) Each technology is better for some of them. In both

cases – Prospects selects based on popularity of technology.

Logistics Model of Innovation (Bhargava, Kumar and Mukherjee, 1993) views market as

a matrix with cells. Adopters are supposed to be persuasive in nature. These cells in the

matrix can be occupied by both adopters and non-adopters. When the process of diffusion

starts all the cells will be filled with non-adopters. But among the non-adopter there might be

a small percentage – who are ready to experiment or innovative. If convinced about the

benefits non-adopters become adopters and they will persuade other non-adopters also to

embrace the innovation. As the time progress, non- adopters will be surrounded an all sides

by adopters and the pressure to adopt will be so high that they do not have other options but to

adopt. The adoption reaches a saturation point, when the technology ages or when the

competition sets in with better product. Adopters themselves lose interest in the technology



and ultimately abandon it. This is a simple model of innovation which explains the growth

and decline of innovation.

Haunschildand Miner (1997) explains the concept of “Trait imitation” in diffusion

process. In this concept, the adoption process is not affected by the benefits of the product,

word of mouth or price. The adoption is due to the fact that some others with a particular trait

or traits have adopted it. Individual are attracted by the “traits” of the adopters rather than

product benefits or features. Traits of adopters like Size and Reputation affects diffusion.

Kano Model (Kano, 1984) proposed this concept while working with “Innovative

products division” of Konica cameras. He argued that innovation opportunities arises mainly

from the latent needs of the customers not just by listening to what they are verbally

expressing.

Figure 4 Kano Model of Customer Satisfaction (adapted from Kano, 1984)

Kano posits three different needs of customers which an innovative product should fulfill,

in order to be successful in the market.

1. Basic need –These are needs which customer assumes that a product will fulfill. If

an innovative product cannot meet these needs, it simply cannot survive in that

market. Any compromise on the quality of basic features of the product, customer

will become disgusted and abandons the product. These needs are “Qualifiers” in a

particular market. One point to be noted is that meeting these “Basis Needs” will

not enhance “Satisfaction” to a greater extent – but it is a question of survival in a

particular market

2. Performance need – The customer judges the quality of a product based on to what

extend “Performance needs” is satisfied. There is a linear relationship between

Customer satisfaction and Performance need fulfillment. “Satisfaction” increases

proportionally with “Performance” of a product.

3. Exciting Need – These are needs which customer won’t expect a product to fulfill.

Fulfilling these needs delights customers. The product should offer some “Extra”

benefits to the customers and exceed their expectations. This makes a product

“Winner” in a particular market. Satisfaction increases exponentially when these

needs are satisfied.


1981-1999)


Today’s market is very dynamic. The customer needs keeps on changing. As the market

becomes competitive, new “Exciting needs” and “Performance Needs” arises. The current

exciting needs become performance needs and performance needs become basic needs as the

technologies advance. At times, even exciting needs becomes basic needs due to stiff

competition or new innovative product introduction.

Theory of planned behavior (Ajzen, 1985) is an extension of TRA. One more component

- “Perceived Behavioral Control” was added to enhance the behavioral predictability of TRA.

Perceived Behavioral Control can be defined as the extent to which an individual thinks he

can successfully perform a behavior in a particular context. These studies were based on the

concept of Self efficacy from Social Cognitive Theory (Bandura, 1977). So according to TPB,

apart from attitude and subjective norms, perceived behavioral control also affects the

formation of behavioral intention. Confidence on “Self-Ability” to perform a particular task

may affect the Purchase decisions.

Figure 5 Theory of Planned Behavior (adapted from Ajzen, 1985)

The factor – Perceived Behavioral Control is very relevant in the field of Marketing and

Sales of Technically Sophisticated products. The companies selling sophisticated products

should high light their “user friendliness” and “ease to learn”. Otherwise customers will have

a feeling of skepticism towards innovative products. They will feel themselves incompetent to

use – if the companies project an image of technical complexity.

Taylor and Todd (1995) introduced this model to further explain the components of

Theory of Planned Behavior (Ajzen, 1975) namely 1. Attitudes, 2. Subjective Norms 3.

Perceived Behavior control



Figure 6 Taylor and Todd Model (adapted from Taylor and Todd, 1995)

Favorable attitude towards a particular product is formed due to advantages it offers in

comparison with the competitors, compatibility with the existing work settings, relative ease

to learn and use the product. Subjective Norms – the “Do” and “Do not’s” of an individual is

affected by the Normative beliefs that he should submit to the opinions of the reference group.

Mass media and Peer group are the two reference groups that can have a significant impact on

Subjective norms. Perceived behavior control – the confidence of an individual to exhibit

certain behavior depends on the conducive environment and his own ability to execute the

behavior.

Hall and Hord (1986) proposed the “Concern Based Adoption model” to implement

“Changes” in organization by addressing the “Concerns” of individuals at various stages of

“Implementation”. People resist changes for lack of necessary skill set to adopt change.

Employees in an organization should have an open mind set to accept change. The employees

will have various concerns during stages of implementation.

Seven Steps involved in CBAM are (1) Awareness (2) Information (3) Personal (4)

Management (5) Consequence (6) Collaboration (7) Refocusing. Employees ask questions at

all these seven steps. Adoption process progresses by convincing the employees about the

concerns at each stage. First step, the concern of the customer as to what the change is all

about should be cleared. Subsequent to that, the employee should be convinced how the

change will affect the organization. The concern of the employee as to how the change will

benefit the individual should be addressed properly. The benefits accrued to the employee due

to this change should be convinced. One of the main reasons the employees resist change is

the lack of confidence on their ability to master the change. This concern can be addressed by

implementing trainings or professional development programs. Next step is to convince the

employee regarding the functionality of change in the organizational context. Theoretical

concepts should work well in practical settings. The employees should be exposed to

examples from other organizations or testimonials from other users about the benefits.

Employees are rational and they will search for alternatives for the changes. Employees

should be convinced that the “Proposed” change is best for the organization and for

themselves to enhance the job performance.


1981-1999)


Figure 7 Concern Based Adoption Model (adapted from Hall and Hord, 1986)

Kwon and Zmud (1987) proposed IS Implementation model based on the study of

Lewin’s Three Stage Model of Change – Initiation, Adoption and Implementation. The main

disadvantage of Lewin’s model was that it did not take in to account the satisfaction derived

by the end user due to system usage which will ultimately lead to abandonment or continued

usage of the IS. The stage of IS implementation proposed are (1) Initiation (2) Adoption (3)

Adaptation (4) Acceptance (5) Performance satisfaction (6) Incorporation. This model

explains the post adoption of innovation behavior. Kwon and Zmud states that most of the

research regarding diffusion/adoption fall in to “factor research stream” – as described by

Zhao and Frank (2003) – “long, almost exhaustive, list of factors that may affect the uses of

technology” without fully understanding the adoption and implementation process. Kwon and

Zmud (1987) identified five major contextual categories of factors that can affect the

technology adoption (1) User community (2) Organization (3) Technology (4) Task (5)

Environment. Kwon and Zmud (1987) did not convey at what stage of adoption these factors

impact and the intensity of impact.

User community characteristics proposed are Job tenure, Education, Resistance to change.

Organization Characteristics: Specialization, Centralization, Formalization. Task

Characteristics proposed is Task uncertainty, autonomy, task variety, Responsibility of the

person handling the job. Organizational Characteristics proposed are Uncertainty, inter

organizational dependence.



Figure 8 Kwon and Zmud Six Phase process (adapted from Kwon and Zmud, 1987)

Cooper and Zmud (1990) proposed a Six stage model for IS implementation based on

earlier works done by Kwon and Zmud (1987). These stages need not follow a sequential

pattern and at time many stages occur as parallel processes. Study revealed that many factors

affect each stage of adoption. Sometimes same factor affects multiple stages. So it is

important to investigate in detail each stage of adoption with respect to an industry or

organization and to identity the relevant factors affecting each stage.

Figure 9 Cooper and Zmud Implementation Process (adapted from Cooper and Zmud, 1990)

Homer’s Feedback Model of Adoption (1987) emphasizes the need for feedback loops in

the adoption process to sustain the adoption of innovation. Homer (1987) studied the diffusion

of medical technology in hospitals concluded that two feedback loop affects the diffusion

process. First is the feedback regarding the current users regarding the product or inputs

relevant to product from other sources. Second is the feedback based on individual perception,

Structure and strategy of the organization. Based on these two feedbacks, the individual

adopts a technology.


1981-1999)


Figure 10 Homer’s Feedback Model (adapted from Homer, 1987)

Hays (1996) posit that “Re-Invention” of “Product features” and “Product usage” can

accelerate the adoption of innovation. The business environments and the requirements of the

customers are very dynamic nowadays. The requirements keep on changing. Moreover, quite

often, the innovations are threatened by other innovations. Re-Invention is the process of

taking feedback from the market and to modify existing features or to add new features or to

find a more effective way of using the product or to find new applications for the existing

product. The concept of “Re-Invention can be applied to Innovation ideas, Innovation

Products and Innovation Practices.

Ellis’s (1989) explains the diffusion process in six steps. At the starting point is the

customer will recognize the unfulfilled need. He starts searching for relevant information.

From one source, based on the advises received regarding other helpful sources, he will

“chain” to other sources. In the third step, customer “differentiates” relevant information from

irrelevant. He selects the relevant and discards the irrelevant. Next step is the verification of

the information collected based on stories or experiences from the market or based on inputs

from opinion leaders. Positive verification results in the adoption of the emerging technology.

Figure 11 Ellis Model (adapted from Ellis, 1989)

Browsing and monitoring refers to the process of analyzing the external environment

beyond the control of the customer which can modify or abandon the need

Cognitive Fit Theory (Vessey, 1991) states that for a customer to select a particular

product to solve his problem, there should be a match between how the customer has

perceived the problem/requirement in hand and how he perceives the features of the product



will help him to solve the problem. If there is a match, the customer will definitely buy the

product. Otherwise he will reject the new idea completely.

Figure 12 Cognitive Fit Theory (adapted from Vessey, 1991)

So the companies must position their products in such a way that the match as suggested

by this theory is achieved for better adoption in the market.

Burkman (1987) developed the User Oriented Instructional Development Model to

facilitate the adoption of “Instructional technologies” in Educational Sector. He proposes a

five step model to accelerate adoption process. (1) Identifying your Target Market is first step.

The companies should be capable of addressing the requirements of this market in an efficient

and effective manner. (2) Next step is to understand how the target market views the

invention or their perception about how the invention/proposed invention should address their

requirement. (3) Manufacturing a Prototype based on the feedback of the prospects. The

prospect should find it easy and interesting to use. (4) Once the Prototype is ready, the

prospect should be informed about its availability. Prospects should be encouraged to buy the

product by facilitating opportunities for “Trial”. (5) Once the product is purchased, good

after-sales support should be provided in terms of training, warranty repairs through 24 x 7

call centers. Many innovators lag behind in after sales support. In their hyper excitement to

introduce the invention, they forget to set up necessary adequate infrastructure or manpower

for aftersales service. In today’s competitive market, customer ditch products for poor after

sales service. After sales service helps to develop brand loyalty (Koskela and Howell, 2002)

Sharif and Ramanathan (1981) divides the market to four categories of adopter sets for

innovative products. 1. Rejecters 2. Adopters 3. Disapprovers 4. Uncommitted. The four

categories are formed based on the relevant information about the product from the market or

by word of mouth publicity. The process of adoption in the market is explained by the below

figure. The market as such is uncommitted in the beginning for innovative products. As the

“Uncommitted” gets relevant information regarding the innovative product, they either

become “Adopters” of the product or “Disapprovers”. Disapprovers are the set of customers

in the market who opposes the new technology – due to “Fear of change”. The prefer

continuing their “Status Quo”. The adopter gets converted to “Rejecters” based on their user

experience or their changed environment/work settings. Based on “Word of mouth”, at any

point of time, the potential “Uncommitted customers” can become “Rejecters”.


1981-1999)


Figure 13 Sharif and Ramanathan Categorization (adapted from Sharif and Ramanathan, 1988)

Mahajan, Muller and Kerin (1984) studied the influence of positive or negative word of

mouth on adoption and divided the market into three categories – unaware, prospect,

customer. The flow of information – favorable or unfavorable affects the conversion from the

three adopter categories. Unaware segment becomes favorable or unfavorable prospects based

on good or bad word of mouth publicity. Prospects also gets converted to customers or

negative triers based on word of mouth. Customer abandons product based on favorable word

of mouth about other products

Figure 14 Mahajan and Muller Categorization (adapted from Mahajan and Muller, 1984)



Hahn (1994) proposes four categories of adopters in his famous “Four Segment” model.

Adopters are categorized as (1) Non – Triers (2) Triers (3) Post Trial Non repeaters (4) Post

Trial repeaters. Inputs from opinion leaders and perceived benefits facilitate product trial and

Performance and brand reputation affects “repeat purchase”. This is very similar to Rogers

(1962) classification of adopter set.

Hong, Labe and Bayus (1989) segregated the potential market into three categories to

explore the market potential for innovation products. (1) Adoption sales (2) Replacement

sales (3) Multiple purchases. All these categories have unique characteristics. To accelerate

diffusion, different category specific strategies should be adopted. The impact of multiple

purchases on diffusion is often over looked by other models. (Mahajan, Muller and Wind,

2000)

HFW Model (1998) Hardie, Fader and Wisniewski (1998) emphasized the relevance of of

(1) Heterogeneity (2)Never Triers in the process of adoption of innovation. Heterogeneity of

the market is often overlooked by diffusion models. Heterogeneity of markets calls for market

segmentation and different market strategies for segments. The companies can also target the

segments relevant for innovation. “Never Triers” are a market category who cannot be

convinced by any means to use a particular technology of product. Companies have to

identify this segment as there is not point spending resources on this category.

Putsis Model (1998). Most of the models in Diffusion literature assume that the

parameters or diffusion variables changes with time in a “pre-specified” way. These models

assume that the behavior of external variables over a period of time can be predicted. But this

assumption is not true as the business environment is stochastic and keeps on changing. It is

extremely difficult to predict these changes.

Putis and Dhar (1998) proposed a stochastic model of diffusion to accommodate for the

unexpected changes in diffusion parameters over time. Puttis segregates the market in to two

(1) First time purchases (2) Replacement Purchases. The adoption rate in these categories is

affected by (1) Price (2) Existing Stock levels (3) Demographic Variables.

Figure 15 Putis Stochastic Model (adapted from Putis, 1988)

Most of the existing studies on innovation examine innovation from adoption and

diffusion perspective. The Model of Innovation Resistance (Klein, Rock and Evans, 1967)

studies innovation from “Resistance to Innovation” perspective becomes more beneficial

especially for sophisticated innovative technologies. (Booz, 1981). The people who oppose

change might be more rational than people who accept change just for the sake of change.

Resistance to change is a natural phenomenon. (Sheth, 1981). In a market, both acceptance


1981-1999)


and resistance co-exists. The innovation becomes a failure if it fails to overcome the

resistance. (Newcomb, 1953).

Ram (1987) proposes “Model of Innovation Resistance” to study innovation from

“Resistance Perspective”. Resistance to Innovation is based on three factors (1) Perceived

Innovation characteristics (2) Consumer characteristics (3) Characteristics of Propagation

Mechanisms. The most important characteristic of an innovation is the ability to get it

customized as per the customer requirements. Especially when implementing Information

systems, many rounds of “customizations” are done to finally adapt the technology to the

requirements of customers. Series of customizations reduce the resistance gradually step by

step.

Consumer characteristics like self confidence and dogmatism, motivation, attitudes and

beliefs, Previous Innovation experiences affect the adoption decisions. Higher levels of

dogmatic tend to oppose innovations. (Rokeach, 1971). Better clarity, credibility,

informativeness and source attractiveness lowers resistance to innovation. Robertson (1971)

posits that in the initial phases of innovation, the effectiveness of information propagation by

the innovator affects the adoption but in the later stage consumer reports and word of mouth

plays an important role.

In Metcalfe Model, diffusion is seen as a change between equilibrium levels by price

adjustments and changing environments (Metcalfe, 1987). There are two types of

Equilibriums (1) Supply diffusion (2) Demand diffusion. These two equilibriums are always

kept in balance by price and cost adjustments. When supply is higher than demand, the price

is lowered to attract customers. When demand is higher, the price is increased for more

profitability and to restore the balance between supply and demand. Diffusion of innovation is

the transition from one state to the other by various factor adjustments

Wilson (1981) explains the information seeking behavior of individuals. The individual

searches formal or informal sources of information till they get information relevant to their

need. They receive either partial information or full information from various sources. The

search continues till they get complete information that can fulfill their need. In the process of

information search, they pass on the information to others as well.

Figure 16 Wilson’s Information Seeking Behavior (adapted from Wilson, 1981)



Companies with innovative products should give relevant information regarding the

product to the target market. They should try to convince the “Opinion leaders”, who in turn

will convince others in the market.

Grey Prediction Model (Deng, 1982) explains the diffusion process when the customer

does not have all relevant information at his disposal to embrace adoption. This model has the

ability to self-adapt in different contexts. It helps to predict the diffusion process of an

emerging technology when there is uncertainty regarding the data. The model uses a time

series analysis to explain diffusion. A system is “WHITE” if all the pertinent information are

known regarding the system. Otherwise it is called “BLACK”

Grey prediction, in the context of in complete information regarding a particular state,

uses the output from the preceding state as input to predict the output of that particular state.

If relevant information regarding an emerging technology is not available, the information

regarding the similar technologies are used to explain adoption process.

Mathematically the model can be represented by a differential equation.

Where X = state, a = developing coefficient, b = Grey influence coefficient Hauser and Wisniewski (1982) contradicted the notion that the purchasing behavior of a

customer for a particular product category can be predicted based on his last purchase. Hauser

and Wisniewski (1982) posited that the purchase intention depends on “behavior states” –

namely awareness and unawareness. Initially the companies have to move the customers from

a state of “unawareness” to “awareness” about product benefits. Then stimulate the customers

to make the buying decisions. This model was based on the study of diffusion of Public

Transport system in Chicago.

Figure 17 Hauser and Wisniewski model (adapted from Hauser and Wisniewski, 1982)


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The innovation adoption is affected by 1. Direct email 2. Publicity 3. Word of mouth 4.

Preference 5. Availability 6. Budget allocations 7. Inertia.

First three factors – 1, 2 and 3 – helps to create awareness about the product in the minds

of customer. From awareness, the customer should graduate to a state of “favorable attitude”

towards the product. This favorable attitude or preference towards product is affected by its

User-friendliness, safety features and work setting adaptability. Next step is to make the

product available to customers either directly or through accessible distribution channels.

Cost- benefit analysis or ROI calculation of the innovative product or losses incurred due to

obsolete technology facilitates budget allocations. An assurance from the manufacturer side

regarding the “Training on the equipment usage” and “After sales support” helps to do away

with the “Fear of New Technology”.

Not many diffusion models have explained the effects of advertisements on Innovation

adoption. Horsky and Simon (1983) proposes that advertisement enhances the innovativeness

of the customers. The model was based on the studies conducted on the adoption of

“Telephonic Banking” services, considered as innovative in 1970s.

Mathematically the concept is modelled as follows

S(t) is the sales at a particular point of time, A(t) = Advertising at a time “t” and b =

effectiveness of advertisement. Advertisements enhance the “Inquisitiveness” of the

customers and this “Inquisitiveness” has to be converted to “Sales” by imparting relevant

benefits about the products. Horsky and Simon model (1983) explained the effects of

advertisement on the innovative component of adoption. They were silent on the imitative

component of adoption. Simon and Sebastian (1987) argued that the advertisement enhanced

the innovative and imitative components of adoption.

Media can play an important role in conveying the information about innovation to the

listener. The media should be able to reproduce the information at the listeners’ end without

any distortions or omissions – in fact it should be reproduced at the receiver – as it was send

from the other side. The ability of a media to reproduce the information send over it is called

“Media Richness” (Daft and Lengel, 1986). Media Richness theory helps to assess and rank

the media based on its ability or richness to reproduce the information. The more learning that

can be pumped through a medium, richer the medium. So to speed up adoption of innovation,

a communication medium/media should be chosen based on how communicative they are –

that is based on the information conveyed and media richness of the medium.

Dixit, Dixit and Pindyck (1994) proposed the “Real Options Frame work” to explain the

adoption of innovative technologies. According to them adoption decisions are influenced by

three factors (1) Uncertainty about the benefits (2) Irreversibility once a decision is made (3)

Option to delay. The individuals adopt only when they become convinced that the benefits are

greater than the costs. This causes a delay in adoption. In real option model – the prospect has

a “call option” to adopt and they exercise this call option when they are convinced about the

benefits of the technology. Luque (2002) confirmed that “uncertainty” characterizes the

adoption of innovative technologies like CAD/CAM and robotics in US manufacturing plants.

“Change is the only constant”. The factors influencing the consumer behavior keeps on

changing. Today’s information will become irrelevant tomorrow. Future forecast are made at

a particular point of time. But as time passes by – the purchasing intentions of customers

changes. These changes are not updated in the forecasting models. Augmented Kalman Filter



(Xie, Sirbu and Song, 1997) takes into account the changes in the market and increases the

accuracy of forecasting. It constantly updates as and when the new information arrives.

Soule and Strang (1998) propsed Epidemic/Learning model to explain the adoption “S”

curve. Consumers have identical tastes and the cost of innovation remains constant or

decreases monotonically as the time progresses. Not all adopters have access to information

about innovation. As a result, the adoption is slow in the beginning. As the time progresses –

more and more people gets access to information either from neighbors or through some other

channels – the adoption accelerates. Beyond a certain point – saturation sets in and adoption

rate decreases again. This model also predicts a “S” curve of innovation diffusion.

Kalish (1985) argues that a customer always attach “Uncertainties” with an innovative

product. These uncertainties affect the adoption of innovation. The uncertainties regarding the

features of the products, whether the product meets the quality requirements, whether the

product is worth paying – affects the purchasing intentions of the customers. These

uncertainties decreases as more and more customers adopt the products. Advertising and

Word of mouth publicity facilitates the flow of information regarding the innovative product

in the market. The companies introducing innovative products should do away with the

uncertainties in the minds of customers by providing them relevant information.

Mathematically the model is represented as follows

Where Y(t) = Cumulative Sales up to time “t”, Pr(t) = Price at time “t”, A(t) = Advertising

effect, I = Information or Awareness level, m = initial market potential, g and f and u are

function operators, b and b’ and k are parameters.

Rolling (1988) states that to accelerate the process of adoption (among Farmers), the

target market has to be exposed to the relevant information about the innovation through

appropriate communication channels. So the two important factors which can affect the

adoption are (1) Target Market Specific Relevant Information (2) Communication channels.

“Market Specific Socials networks” can do wonders if utilized smartly.

Nowark (1992) suggests that Farmers are unable to adopt innovative technologies due to

lack of information, Technology sophistication, inadequate managerial skills, high labor

requirements and lack of supporting resources (Mandel, 2010). The right information can

provide a significant role in the adoption process. (Feder, Just and Zilberman, 1985). The

information about the innovation should be above the threshold level of farmers to trigger

adoption process (Saha, Love and Schwart, 1994). Farmers always relies on multiple sources

of information prior to adoption (Velandia, English and Martin, 2010). Simple technologies

enhance adoption (Batte and Arnhorld, 2003)

Steffen (1998) proposed “Multiple Ownership Diffusion Model” to explain the influence

of repeat purchase of the innovation by the adopters. The diffusion rate can be accelerated by

encouraging repeat purchase by the existing customer set. The task is easier than cultivating

new clients. Company can impart information regarding the “New Uses” of the same product

which increases the frequency of use and there by stimulate “Repeat Purchase”. In this model,

both adopters and non-adopters are viewed as potential buyers.

Mathematically,


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Where V = Total Number of “Repeat purchases”, Pi = “Repeat Purchase Adopters”, p and

q are innovation and imitation coefficient of second purchase.

“Reservation price” is the “maximum price” a customer will pay for a product. Jeuland

and Dolan (1981) introduced the concept of Reservation price to diffusion literature. The

study argues that the prospects always attribute certain uncertainties to the performance of a

product. Prospects believe that the Innovative product will not perform as assured by the

manufacturer and always assumes a lower “Reservation price”. If the non-adopters in a

market are well connected, the favorable “Word of mouth” from adopters changes the

perception about the innovation and Reservation price goes up. Relevant information through

other channels can also produce the same effect.

Lieberman and Paroush (1982) argues that the adoption rate is influenced by Income

diversity, Marketing campaigns and Price. Income of the adopters and Price of product

determines the affordability of a product or a set of products. Since a market consists of

diverse income groups, the concept of affordability of an innovation influences the adoption

rate. Income determines the “Purchasing power” of the customers. Marketing campaigns

impart relevant information about the products and even provides opportunity for “Hands on”

with the instrument, thus reduce uncertainty regarding the technology and benefits.

Thomas and Teng (1984) studied the diffusion of “State of the Art Telephones” in

Germany and argued that (1) Pricing strategies (2) Scale of Advertising (3) Word of mouth

speeds up rate of diffusion. Repeated exposure to advertising incites a favorable attitude

towards the product. In advertising, a point to be noted is only relevant information should be

conveyed to the prospect instead of information over load. Robinson and Lakhani (1975) state

that “Price skimming Strategy” is not a good idea for innovative products. The price should

be low initially, it can be increased as the market becomes convinced of the advantages and

near saturation point, it should be decreased to increase sales. Effective advertising strategies

triggers chain imitations in the market. (Simon and Sebastain, 1987).

Feichtinger (1985) based his model on “Optimal Control Theory”. He posits that – in

order to attract customers, at the product introduction stage, the product should be priced

lower forgoing the profits and as the time progresses, as and when the product shows signs of

successful adoption, the price can be hiked for want of more profits. A part of the revenue

from price hike should be channelized for increasing the advertising budget to stimulate

repeat purchase.

Mathematically,

Where x(t) = Sales at a point of time (t), a = Innovation Coefficient, b = Imitation

Coefficient, g(p) = Market potential

A company always tries to maximize its profit “J”

Where p = price, c(x) = Production cost and r = discount

Cost of production decreases as the time progresses due to Economies of Scale”



Kamakura and Balasubramaniam (1988) studied the effects of price on the diffusion of

electrical consumer products in households. According to the study price had no effect on the

adoption of inexpensive products and for expensive products, price plays an important role.

Mathematically,

B1=Impact of price on adoption, B2 = Impact of price on market potential, M(t) = Market

potential, 0 = Ultimate penetration level

This model did not take into account the effects of advertising or word of mouth publicity

in diffusion. This model is not based on the concept of “utility” delivered to the customers.

Bottomley and Fildes (1998) analyzed the diffusion patterns of VCDs and concluded that

Price do not influence the diffusion probability. Their studies were based on the model

proposed by Kamkura and Balasubramaniam (1998).

As the time progresses, due to “learning” from the market, manufacturer enhances the

quality or features of the product. Accordingly, word of mouth publicity becomes favorable

and adoption rate increases. (Lilien, 1990). The “purchasing power” in a particular market is

an important factor and companies have to access this prior to introduction of an innovative

product. The “Price” should be affordable to the market. Relevant and adequate information

regarding the product stimulates the “Trial” of innovative product. Market should not be over

loaded with information, which in turn confuses customers. “Quality concerns” should be

properly addressed through “Hands on” demonstration or “Testimonials”.

Mathematically,

S(t) = New adopters, X(t) = Cumulative adopters, N(t) = Market potential, P(t) = Price,

h(p) = Fraction of market potential with acceptable price, A(t) = Information level, Q(t) =

Perceived quality, R = Response to Information level.

Jain and Rao (1990) devised a model to assess the effective market potential of an

Innovative product at a specific time as a function of price. He tested his model with low

value products and high value products and found that market potential price relationship is

valid for high value products. Price affects the rate of adoption for high value products.

Mathematically,

Where S(t) = Effective Market potential at time “t”, Y(t) = Cumulative sales.

Golder and Tellis (1998) explains the concept of “affordability” in the adoption of

innovative products. An innovative product might be very much beneficial to a customer but

it might not be within his budget. So he will wait till the innovation becomes “affordable” to

him. Otherwise his income should increase to afford the innovation. So it is important for the

manufacturers of innovative products to assess the purchasing power of the market or

segment.

Customers are always skeptical regarding the performance or suitability of an innovative

product. They always want to reduce the “risks” associated with their purchasing decisions.

Chatterjee and Eliashberg (1990) introduced the concepts of “Risk hurdle” and “Price

Hurdle”. An innovative product should overcome these two hurdles to become successful in


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the market. The product should meet the requirements of customer. At the same time, it

should be affordable to the customer. Customer sets a bench mark based on the inputs from

the market. Companies should meet both these expectations of these customers. The

innovators should reach a compromise regarding the “Performance” and “Price”.

David (1990) posits that the delay in diffusion of electrical power and computers in USA

is due to the “Cost of adopting new technology” rather than the cost of technology itself. Cost

of adopting new technology includes the cost of training the employees on the new

technology, complimentary investment in infrastructure and cost of routines reorganization.

“Complimentary investment” impacts adoption along two dimensions. (1) It increases cost

and slows down diffusion (2) The time taken for implementing technology infrastructure

slows down the rate at which innovation benefits are perceived by the end user. (Mowery,

1998).

Biddle and Seow (1991) studied the effects of “Snob effect” on diffusion of innovative

products. “Snob effect” reference to the process by which the demand of expensive product

(Rarely available) decreases as price decreases (Easily available) and the number of adopter

increases. The possession of these products projects an image of belonging to a “Social class”.

As the adopters increases the product losses it “Rarity Value” and “Social class

Belongingness appeal”. So the rate of adoption decreases. Lesser the availability of the

product, higher the snob value. Products exhibiting Snob effect have little or no utility. The

inner urge of human beings to possess rare and expensive items or unique experiences creates

snob appeal. Biddle and Seow (1991) conclude that higher number of initial adopters might

not be an indicator of the success of an innovative product due to snob effect.

The effects of “Available Product Substitutes in Use” on the adoption rate are

conceptualized by Srivastava, Leone and Shocker (1981). The model investigated the

diffusion patterns of Gas Stove Vs Electric Stoves and concluded that “Interacting” products

have a negative influence on each other. The model advises the “Product Substitutes” to focus

on some market niches for survival in the highly competitive market. The market for some

innovative products will be dependent on related products. This process is termed as

“Contingent innovation” (Bayus, 1987). These adoption rates of these products can influence

each other (Sengupta and Bucklin, 1993). The sales of “Innovative cartridges” depend on the

“Sales of Printers”. The innovative cartridges reduce the usage cost of printers/better printing

quality. The sales of printers increase the use of innovative cartridges. These kinds of

“Contingent innovations” revolutionize the way the complimentary products are used

together.

Norton and Bass (1987) studied the adoption of successive generations of sophisticated

technology products in the market. Some of the customers might be using the technology for

the first time. At times, the new technology replaces the old technology. So the adoption

process is affected by the “Replacement purchase” of the existing adopters and “First time”

purchase of potential adopters. In fact – “generations” compete against each other.

Mathematically this can be represented as follows,

S(2)(t) = F(2) (t –r2) (m2 + F(1)(t)m1)

Where S(t) = Sales of a particular generation, F(t) = Fraction of adoption for generation,

m = market potential, r = Time of introduction.

Mahajan and Muller (1996) states that in some markets customer adopts a particular

generation of product and discards other. This happens due to the fact that the customers are

not sure regarding the performance of an innovative products and they wait till the technology

matures. Another reason can be that the companies make the product so complicated that the



latest generation will not be user friendly like the older generation technologies. Kim, Chang

and Shocker (2000) states that the marketing strategies of competitors also affect the adoption

of successive generations. Price also affects the adoption of these products (Putis, 2001)

Kumar and Kumar (1992) developed three models to explain the Technological

substitution effects on diffusion. The first two models explain the effects of imitators and

innovators on adoption rates. The third model explains the effects of promotional subsidies on

adoption rate. First two models were based on Smiths model of population dynamics (1963).

The Third model was inspired by Fisher – Pry model. Promotional subsidies for the diffusion

of an emerging technologies are (1) Reduced prices (2) Interest Free Loans (2) Cash grants

and (4) Subsidized rents. Promotional subsidies help to overcome the cost barriers for

emerging technology.

Bayus (1993) examined the diffusion of innovative consumer durables and concludes that

the best predictors for replacements are (1) Perception about the product obsolescence (2)

Working condition of the durable (3) Working status of wife (4) Expected increase in house

hold income.

Predator – Prey situation occurs when a new technology (The Prey) positively influences

the increase in sales of an established product (The Predator) and this phenomenon in turn

retards the growth of another emerging technology. (Moore, 1993). The attack of Netscape on

Micrsoft windows (The Predator) forced Micrsoft to enhance their features to improve

performance.

Kim, Chang and shocker (1998) critically analyzed the patters of diffusion of Pagers and

Mobile phones in Hong Kong market and argues that inter - category and inter – generational

competitions can have impacts on the adoption rates. New models face intense competition

from the old models especially when the time gap between introductions is very short. This is

very relevant in the cases of Mobile Phone and Laptop computers. This model also explains

about complementary and substitution effects in inter - category products. Pagers will have a

complimentary effect on the diffusion of mobile phones at the same time mobile phones

retards the adoption rate of Pagers.

Gates (1998) states that for complementary products innovation in one category can lead

to increased sales in other category. This model studied the adoption rates of “Application

Software” and “Personal computers and found to have a Reinforcing influence on each other.

The study also posits that combination of all complimentary functions can create new

innovations and the likelihood for success is very high. Gates cites the example of “Mopiers”

which can print, scan and fax, do all the functions of printer, scanner and fax machines.

Mopiers increases the convenience to customers. Production of Mopiers produces economies,

since some of the hardware used for printer, scanner and fax machines are common. The

“Mopier” will be cheaper than the costs of all three combined.

AI Winter is a concept originated from Artificial Intelligence. Minsky (1986) states that

all industries/sectors follows “Hype cycles” – that is periods of Optimism, appreciation and

funding followed by periods of Pessimism, Criticism and Cut backs on funding. Hype cycles

triggers chain reactions of Optimism and Pessimism. Optimism starts in a particular industry,

spreads to media, reaches government and allocates funds for research. Similarly, Pessimism

leads to cut back on funds and affects serious research. Hypes are often observed in

innovative technologies (Brooks, 2002). The concept of AI winter – changes the perception

about the emerging technologies in the minds of customers. Customers are generally

influenced by the hype cycles or common technology trends in the market. So, the companies

introducing emerging technologies should be aware of the hype cycle though which the

industry is going through. Hype cycles affects the diffusion of innovation.

https://en.wikipedia.org/wiki/Marvin_Minsky

https://en.wikipedia.org/wiki/Rodney_Brooks


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Business environment is always dynamic. “Theory of Management Fashion” proposed by

Abramson (1991) states that under environmental uncertainties, business entities adopts

practices or management styles promoted by “Fashion Setting Organizations” like

Management Gurus, Consultants and Business Media. Arguments of Rationality,

Compatibility or Technical Efficiency cannot explain certain adoption decisions. The main

factors affecting the adoption of fashion are (1) Status of fashion setters (2) Sources of

disclosures (3) External pressure for change like competition, regulations etc. The use of

specific practices or styles decline over a period of time and new fashions emerges and the

cycle continues. Management Fashions can be studied from two perspectives (1) Discourse

life cycle – The volume and nature of discourse over a period of time by bibliographic or

content analysis. (2) Diffusion life cycle – The extent to which innovation is adopted by

organizations by various surveys, case studies and secondary data analysis.

Radas and Shugan (1998) introduced the concept of seasonality in to diffusion literature.

Seasonality increases sales of a particular product category in the particular season or duration

in a year. Air conditioners are sold in the months of summer. Without taking the concept of

seasonality, the managers had to wait for years to get the trends or patterns in the market. But

the market is very competitive today with many national and international brands that too

with very short product life cycle. Incorporating the concept of “seasonality” helped managers

to analyze the trend with “months of data” rather than to wait for “years of data”. This

concept helps managers to make important decisions with limited data, at times immediately

after the introduction of product.

Network externalities play an important role in diffusion (Farrel and Saloner, 1985).

Network externality refers to benefits accrued to a customer due to the same/similar products

being used by others. The value delivered by a product to a particular person increases or

decreases as the number of adopters of that product increases. As more and more customers

adopts the product, compatibility of the products to the customers increases. A telephone is

useful to a person, if there are other customers also using telephone for communication.

“Positive Externality” refers to increase in value of a product to an individual as the number

of adopters increases. As the number of adopters increases for mobile phones, the value

delivered by the mobile phone for communication increases. “Negative Externality” refers to

decrease in value of a product as the number of adopters increases. As the number of private

vehicles increases in cities, it becomes extremely difficult to find parking in cities. As a result,

individual’s starts using public transport and the value delivered by private vehicles to the

owners decreases.

Horsky (1990) states that an innovative product is adopted by customers if they are

convinced that the product delivers the expected benefits or preferably if it exceeds the

expectations. The expected benefits are in terms of “Reduced time” and “Utility”. One

important feature of this model is that it takes into account the purchasing power of the

customers in a particular market.

W(t) = average wage rate Pr(t) = average market price, K = Time Saving, k = Utility

Enhancing, Y(t) = Cumulative sales, M(t) = Total Market, 0 = Potential adopters, p and q =

Diffusion parameters.



This model explains “Why” of a diffusion, where as other models explain “When” of

diffusion

The adopters in a particular market are heterogeneous. Due to this heterogeneity, the

benefits accrued due to an innovation vary among customers. (Karshenas and Stoneman,

1993). “Rank Equilibrium model” helps to prepare a rank list of prospects based on the

“perceived benefits” to them. The prospect with highest ranking in terms of perceived benefits

adopts before those in the lower ranks.

A demerit of this model is that sometimes the internal or external environment restricts a

prospect from adopting a beneficial innovation. This fact is not accounted for in the model.

Lacovou, Benbasat and Dexter (1995) explain that higher the “Perceived” benefits of

technology, faster the adoption. Locovou model is based on the studies in IT innovations.

Organizational readiness to adopt technology is explained along two dimensions (1)

Sufficient financial resources to procure and support the technology (2) Resources mainly

man power to operate the Technology and the infrastructure requirement for Technology

implementation. Competitive pressure also forces the organizations to adopt an emerging

technology. Sometimes due to pressure from “Partners” – the organizations will be forced to

adopt an emerging technology.

Figure 18 Lacovou Model (adapted from Lacovou, 1995)

Aquiar and Reis (2008) states that the number of competitor adopters and the evidence of

benefits for competitors affects the diffusion of innovation.

Roberts and Urban (1988) states that a customer adopts a product based on the product

attributes. Customers assigns weightage to different product attributes and his selection is

based on the “Highest value” of summation of “Intensity of a particular attribute” and

“Weightage assigned to that attribute”.

Mathematically,

Where Xj = Total Score, Yjk = Intensity of a particular feature in a product, Wk =

Weightage assigned to a particular feature.

While introducing an innovative product, the manufacturer should ensure that all relevant

information regarding the product features are imparted to the customer. They should also


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understand in advance the relative importance assigned to particular attributes by different

segments.

Feder, Just and Zilberman (1982) analyzed the adoption of patterns of advanced

technologies among farmers/ in agriculture and concluded that the important factors

contributing for the adoption are (1) Farm size, (2) Land Tenure System, (3) Credit access, (4)

labor availability, (5) Risk preferences (6) Human capital (7) Access to commodity market.

Governments across the world and NGOs especially working in developing countries can

facilitate faster adoption by giving due importance to these factors. Most of the framing

lands/Grains of the world are from rural areas and it is important that famers adopt latest

technologies that can enhance their productivity. Besley and Case (1995) states that the

important factors affecting adoption of innovative technologies in agriculture are (1) Farm

Characteristics (2) Farmer Characteristics (Smith and Seyfang, 2007) (3) Externalities (4)

Cost of adoption (5) Credit availability. They further divided Externalities to (1) Network

Externalities – How many have already adopted? (2) Market Power Externalities – Whether

the adoption gives First mover advantage (3) Learning Externalities – Learn from the

experience of others. Study was conducted among the Corn producers for the usage of

agrichemicals. The probability of adoption of PA was higher with farmers having larger farm

size, higher income and computerized record system. (McBridge and Daberkow, 1998)

Slappendel (1996) proposes a frame of three perspectives to visualize the adoption of

innovation in organizations - (1) an individualist (2) a structuralist (3) an Interactive process

perspective. The first perspective focuses on Leadership, Champions and Pervious Exposure

(Plumb and Kautz, 2014). Structuralist perspective focusses on Organizational Size,

Competitors, Government Compliance and Regulatory requirements.(Plumb and Kautz,

2014). The third perspective views “innovation as a conscious and dynamic process produced

due to that continuous interaction of individual member, organization and the environment.

(Sarosa, 2012).

Scherer (1986) proposed Matching Person and Technology Model to understand the

adoption of emerging technologies with reference to individual organizations. Company

management, at times procures innovative products/emerging technologies to enhance the

work performance. But the employees due to their personal preferences and individual

characteristics will show reluctance to accept the new products. Sometimes the reluctance can

be due to lack of training or support from the part of organization. So the companies, while

adopting new products/methods/technology, should analyze the preferences, characteristics

and competency levels of the employees. Then only then should formulate an internal

adoption strategy.

Task Technology Fit theory states that a customer will buy a product/technology if and

only if the features of the product cater to his requirements in a particular work

setting/environment. (Goodhue and Thompson, 1995). TTF measures “Technology – Task

Fit” along eight dimensions: (1) Quality, (1) Locatability, (3) Authorization, (4)

Compatibility, (5) Ease of use/training, (6) Production timeliness, (7) Systems reliability, and

(8) Relationship with users. In fact the product should facilitate enhancement of Job

performance and at the same time it should be effective under a particular work setting.



Figure 19 Task Technology Fit Theory (adapted from Goodhue and Thompson, 1995)

Especially in Industrial marketing – the products should comply with performance as well

as regulatory standards. Both performance and usability affects the buying decisions. Task –

Technology characteristic match leads to better utilization and enhanced performance of the

product. This model is based on the studies done on Information Systems.

Gustafson (1986) explains the factors affecting adoption of innovation by organizations.

He attributes the factors namely (1) Innovation – Organizational Strategy fit (2) Technology

Stewardship in Organization (3) Support from Innovator (4) Capability of manpower to

master emerging technology (5) Continuous monitoring of innovation output. Gustafson did

not take into account the external factors affecting the adoption of innovation. This model is

not extensively tested in the context of organizations by other researchers (Robert and Bate,

2007)

Tani Model (1988) integrates the factors in the company level (Micro level) and Industry

level (Macro level) to explain the diffusion mechanism of advanced manufacturing

technologies which substitutes labor to a great extent.

The factors affecting diffusion are as follows.

1. Cost benefit analysis at firm level – Explains the benefits to the firm at a particular

cost for a technology. Costs and benefits are quantified. To adopt a particular

technology benefits should be greater than the cost to acquire the technology.

2. Economy of scale in user cost – Larger the company, lower the user cost due to

lower fixed cost per user; the fixed cost is spread out to large number of users.

More over due to “Operational efficiencies” the variable cost also decreases.

3. Wage rate gap between large and small companies – Higher the wage rate gap,

more the probability for adopting ways to reduce it.

4. Company size – Bigger companies adopts faster than smaller companies because

they have better resources and infrastructure to adopt the new technology.

5. Decreasing price of advancing technology – The prices decreases due to learning

effect and economies of scale in production

6. Increase of wages rates–Higher the increase in wage rates, more the possibility for

labor substitution by technology.

The model primarily explains how the labor charges can be saved by adopting advanced

technologies. As the diffusion progresses, the customers find innovative applications for the

technology. Diffusion of technology happens from (1) Larger companies to smaller


1981-1999)


companies (2) Diffusion to other industries with same application (3) Diffusion to other

applications within same sector.

The effects of policies and priorities of an organization on the adoption of innovation

cannot be discarded (Yates, 1989). The markets are very dynamic and the environment keeps

on changing. Based on market conditions, the firms will be forced to change their policies and

priorities. Policy and priority changes the “Relevance” of technologies to a firm

Tornatzky an Fleischer (1990) posits that three factors affects diffusion of emerging

technologies in an organization (1) Technology itself (2) Working environment (3)

Organizational context. Benefits of the Technology, its user-friendliness, availability and

affordability affect the adoption process. After-sales support and the reputation of the firm

providing the technology also have a favorable impact on the adoption. Pan and Jang (2008)

state that infrastructure available in the firm for the new technology implementation affects

the speed of adoption. Liu and White (2008) posit that the skill set to operate new technology

and attitude of employees towards new technology affects adoption process. Teo (2010)

stressed the importance of resolving all existing technical problems prior to implementation of

technology. Thong (1999) states that CEO’s Innovativeness and Knowledge of Technology

affects adoption.

Government regulations and Competition in the industry are other variables which affects

adoption of innovation. (Kuan and Chau, 2001) In the context of intense competition, firms

are always on the lookout for quality enhancement and Cost reduction. At times, the

Government regulation makes it mandatory to implement some technologies.

Size of the firm, its communication channels, the commitment of the Top management to

bring about a change and lack of effective implementation strategy also impacts diffusion of

innovation. (Teo, 2008). Zhu (2008) establishes that in large organizations, the readiness of

business partners also affects the adoption of technology. Anticipated changing trend in

market encourages some organizations to change (Chong, Liu and Raman, 2009)

Chau and Tam (1997) argues that the pressure to comply with Industry standards and

perceived barriers to progress urges organizations to search for cutting edge technologies.

Perceived barriers to implementation slow down adoption.

Figure 20 Chau and Tam Model (adapted from Chau and Tam, 1997)

Henderson and Clark (1990) contradicts the findings of Arrow (1962) by stating that

larger firms have complex multiple levels of bureaucracy and decision making process is



impeded. Moreover, it is extremely difficult for larger firms to change their existing

procedures to adopt innovation. Larger firms will have more resources sunk in their old

technology than smaller firms. Being a part of “large” network also retards the adoption due

to compatibility issues of technologies between different actors in the network.

Levinthal (1990) argues that the speed of adoption can be enhanced by increasing the

absorptive capacity of the firms. Absorptive capacity of the firms can be enhanced by

trainings, convincing employees about the benefits, redefining the roles and changing the

structure if required. Increasing absorptive capacity reduces the “Resistance to change”

syndrome. Absorptive capacity – the ability to learn new technologies can be enhanced only

incrementally. “Routines” embedded in the organization or “Organizational Memory” cannot

be changed radically (Nelson and Winter, 1982). In course of time, the knowledge base and

the routines “Co-evolves” in an organization.

Francik (1991) examined the adoption of multimedia technologies in early nineties and

suggested that certain issues are to be sorted out to speed up adoption. Presence of these

issues retards adoption.

1. “Where” and “How” the technology will be implemented should be crystal clear

2. Technology should be positioned in such a way to “Enhance the Individual’s Job

Performance” not as a threat to the Job security

3. “Cooperative people with right attitude” should be selected for Trail Runs

4. The Roles should not be changed abruptly to create clashes in the organization. If

at all the jobs are to be reassigned, it should be done slowly, with support and

consideration

5. Lines of communications should be clear and any concerns regarding the

technology should be clarified

6. Skill set of the employees should be assessed and training programs should be

conducted to bridge the gaps.

7. The importance of “Training programs” should be conveyed to employees and

they have to take part whole heartedly to update their skills.

Arthur (1994) states that there are companies who embraces innovation at a very early

stage of innovation for getting “First Mover Advantage” – for getting enormous profits from

the market. In the process there exists a tendency for the firms to get “Locked” into a

particular line of technology and discard other emerging technologies. They develop a sort of

shortsightedness. In the long run they fail to keep abreast of emerging technologies and their

competitive advantages as first mover declines or eventually get eliminated. Organization

require a ““pluralistic leadership “rather than a “Unitary Leadership” to assess all competing

technologies and select the befitting technology according to the variability in the

environment. (Van de Van, Garud and Venkataraman, 1999).

Adoption of an innovative technology is based on two factors (1) Tendency to reduce risk

(2) Anticipated Usefulness (Arthur, Lane and Durlauf, 1994). Adopters themselves collect

information from various sources and supplement this with the inputs from the existing

adopters.

Adopters use three “outcome” rules to make a decision.

Max rule – Adopters selects the product that delivered the highest value to an adopted

category

Mean rule – Adopters selects the product that delivered the highest mean value to an

adopted category


1981-1999)


Min rule- Adopters selects the product that delivered the highest minimum value to an

adopter category.

Greenwood and Yorokolgu (1997) argues that adoption of innovative technologies

requires organizations to acquire different skill sets, complimentary resources and changes in

the existing procedures and systems. So adoption in the short run reduces productivity even

though in the long run the productivity is accelerated (Hornstein and Krusell, 1996). The

diversion of existing resources for adoption takes a toll on productivity in the short run

(Helpman and Trajtenberg, 1998). Helpman and Rangel (1999) posits that when a new

technology is adopted, it makes the “Existing Technology specific skill set” irrelevant and

requires the organization to acquire new skill set to use the innovation. So, during adoption of

innovation, the state of affairs getting worse is a temporary phenomenon, followed by an

accelerated betterment. This phenomenon is explained by a J Curve. (Cheng, Lam and

McNaught, 2006)

Figure 21 The J Curve (adapted from Greenwood and Yorokolgu, 1997)

Keller and Forehand (1996) argues that the factors affecting adoption of innovation in a

society are (1) Absorptive capacities (2) Ability to deploy and use technology (3)

Environment (4) Values, Attitudes and Believes (5) Economic Incentives to adopt. The most

important factors affecting the diffusion of innovation in a country are the education level and

skills of its work force (Baumol and Benhabib, 1989). Low risk aversion, High Propensity for

innovation, Education and skill of work forces increases the absorptive capacity of the society

in general (Cirscuolo and Narula, 2008)

Libertore and Bream (1997) Investigated the factors affects the diffusion of Imaging

Technology in Insurance companies and argued that the only factors affecting the adoption

rate are the “Size and Technological Infrastructure” of the organization. Bigger firms with

better Technological infrastructure adopted innovation faster than others. Shepard and Saloner

(1995) studied the diffusion of ATM Technology in banks and concluded that Banks with

large number of branches adopted ATM faster than banks with smaller number of branches.

This was contradictory to the traditional views in vogue at that time that smaller banks require

ATMs to canvass customers and the general perception was that smaller banks will adopts

adopt ATMs faster than bigger banks. This study emphasized the importance of higher

network value and the need for a large firm to act as an intermediary between technology and

customer. “Economies of scale” is also a reason why banks with larger deposits/branches

adopted ATM Technology. As the customer base increases, fixed cost of providing services

per customer decreases. Majumdar and Vankataraman (1998) studies the replacement of

mechanical switches with electrical switches in US telecommunications and found out that

bigger firms adopted innovations before the smaller firms because bigger firms have more

customers and the when distributed, cost per customer will be lower for bigger customers.



This model over looked the need for infrastructure and training when implementing new

technology.

Amara (1983) posits that a customer tends to overestimate the benefits of the technology

in the short run and underestimate the benefits in the long run. An innovative product or an

emerging technology will not be giving immediate results but in the long run it might yield

desired results. But the customer want immediate results. Failure of an instrument to deliver

substantial benefits immediately after adoption may slow down diffusion of innovation in the

short run. To accelerate the adoption process, the manufacturers should high light on the long

terms benefits and make the customer aware about the realistic short term benefits to prevent

disillusionment.

While introducing a new product or technology to a market companies must see to it that

all features of components of the technology are working perfectly. Malfunctioning of a

single feature or component can stall adoption. This concept was termed as “Reverse Salient”

by Hughes (1983). This can also happen when the technology is not as advanced as per the

expectation of the market. Rosenberg (1988) states that absence of facilitating conditions can

also stall the adoption.

Market conditions like “Prevailing Wage Rates”, “High Education Level” and “Skill Set”

affects adoption of innovative technologies (Hannan and McDowell, 1984). Higher manual

labor rates accelerate the adoption of ATM technology as ATMs substitute manual labor.

“High education level” and “Skill Set” helps “Prospective adopter” to get accustomed to the

new technology and to use it.

Tushman and Anderson (1986) proposed the concept of “Competence destroying

Technical Change”. Some innovations even though very beneficial challenges the existing

technology or routines of an organizations and these innovations are viewed as “Competence

destroying Technical change”. In fact these “CDTC” can happen internally. Some innovation

happens as unexpected out comes of RandD and might be very promising if commercialized.

But the firm will not do so as they find it irrelevant to their current line of business.

Ergas (1987) states that the “Topmost” priority to speed up the diffusion is to formulate

policies to overcome the obstacles to diffusion. Some of the obstacles are common to all

innovative products but there are some obstacles which are very specific to some products

only. There should be different “Innovative Policies Mix” for different technologies.

Innovative policies should be customized for each product based on its obstacles.

Ritz and Morgan (1991) studied the influence of distribution channels in diffusion of

products. They state that there are two types of diffusion in the market (1) The diffusion

among distribution channels (2) Distribution among the “End Users”. According to this study

the “actual end users” and “distribution channels” should be considered as customers. So the

companies should target both distribution channels and end users to facilitate diffusion of

innovative products. Some of the products are only sold through distribution channels. So the

distribution channels should be convinced first to sell the products. The end users should be

informed about the “When and Where” of the availability of the products. Different strategies

should be made to entice “distribution channels” and “end users”.

Vijay, Sharma and Buzzell (1993) posits that a new product can (1) enhance the potential

for the specific product category due to heavy advertisements/promotion (2) Kill an existing

market and create a new one (3) take market share from competitors. This study was based on

two competitive brands – Kodak and Polariod.

Bower and Christensen (1995) attributes “Management Myopia” or “Lack of

Foresightedness” on the part of organization – which lead to the slow adoption of innovation

– especially “Disruptive innovation”. This “Management Myopia” has led to the down fall of


1981-1999)


many established companies in the world. Established companies were comfortable serving

their customers with the existing technologies and disruptive technologies offered little value

to them. As the time progressed, disruptive technologies improved to such an extent that it

threatened the very existence of existing technologies and companies embracing those

technologies.

Narashimhan, Sen and Neslin (1996) posits that the “Trial” of an innovative product (For

FMCG) is dependent on (1) Reputation of the brand (2) Impulsiveness (3) Advertising effects

(4) Features (5) Enticing displays (6) Promotion. Impulsive buying is the unplanned purchase

in response to a promotion or attractive packing or display or a unique feature which triggers

a buying behavior. These products may or may not have value in their daily life. Even though

the customer is instantly gratified, they might regret about the purchase in the future and this

might create an unfavorable impression about that product. Instant satisfaction may turn into

dissatisfaction. Dissatisfaction promotes bad word of mouth publicity and slows down the

adoption. The dissatisfaction with the product might be extended to the brand.

Maidique and Zirger (1985) examined the process of diffusion from a manufacturer

perpective and proposed factors or conditions to be complied with by the marketers to speed

up the diffusion process. Very few models have studied diffusion from marketer’s

perspective.

1. Excellent knowledge of market requirements

2. High Benefit – Cost Ratio

3. Early entry in to market

4. Large advertising budget

5. Strong Management commitment

6. Cross functional Team work between various departments

7. Knowledge of potential competitors

Incubation time is the duration between “completion of product development” and

“Satisfactory sales”. Kohli, Lehman and Pae (1999) emphasized the need of shorter

incubation time. Higher incubation time retards the adoption rate of innovation.

TAM was proposed by Davis, Bagozzi and Warshaw (1989) based on their studies in

Information Systems. TAM explains how a customer adopts or embraces a new technology or

process. A customer is confronted with two main questions: Will the product be useful to me?

Will it be user friendly or easy to use? The two main factors affecting the adoption are (1)

Perceived Usefulness (2) Perceived ease of use. These two factors creates a favorable or

unfavorable attitude towards adoption of a technology.

Perceived Usefulness can be described as the extent to which a customer find a particular

technology satisfying his requirement. Perceived ease of use can be defined as the extent to

which a customer thinks he can use a product with minimum effort.

Figure 22 Technology Acceptance Model (adapted from Davis, Bagozzi and Warshaw, 1989)



TAM is based on the concepts put forward in TRA (Fishbein and Ajzen, 1975 ) and TPB

model (Ajzen, 1985). So while introducing an innovative product – the two features to be

highlighted are (1) its usefulness and (2) its user friendliness. Out of these two factors,

Usefulness of the product has a higher bearing on the purchase intention than the ease of use.

(Davis, 1989). Even if a technology is difficult to handle, but if it enhances the work

performance drastically, the customer will go for it. When you have two products offering the

same usefulness, then ease of use will be a differentiating factor. There are lot of refinements

for this theory. Malhotra and Galletta (1999) introduced another dimension to TAM model –

the social influence concept. Individuals adopts innovation not just based on (1) Perceived

usefulness (2) Perceived ease of use but also on social influences as well. They are influenced

by the group pressure – behavior of the group in which he is a member. Individuals always

prefer to adoption an innovation which is acceptable to the norms of the society or group

Model of PC Utilization (1991).Personal computers were an innovation in 80s and Early

90s. Thompson , Higgins and Howell (1991) devised a model to explain the factors affecting

the adoption of PCs in work place. This is based on Theory of Planned Behavior (Triandis,

1977)

Figure 23 Model of PC Utilization (adapted from Thompson et. al, 1991)

Job fit refers to the relevance of a technology to enhance the productivity of an employee.

Affect toward use defines the “Emotions” like Joy, Hatred etc towards a particular

technology. Long Term consequences refer to the benefits in the long run. Complexity refers

to “relative difficulty” to learn and use an emerging technology. Facilitating conditions refers

to the favorable conditions in the work setting for adoption like management support,

trainings, infrastructure etc. Social factors like norms determine the “Dos” and “Do not’s” for

an individual.

Delone and Mclean (1992) proposed Information Systems Success Model to explain the

factors affecting the successful adoption of Information systems. It help us to “identify,

describe and explain the relationships among six critical dimensions along which the

Information Systems ae evaluated”. It helps us to get a comprehensive picture of IS adoption.

The six dimensions are (1) Information quality (2) System quality (3) Service quality (4)

Usage Intention (5) User satisfaction (6) Net system benefits.

Taylor and Todd (1995) proposed Combined TAM and TPB Model to explain the

adoption of innovative technology by both beginners and experienced professionals in IT

field. Taylor and Todd (1995) added Subjective norms and Perceived behavior control from


1981-1999)


TPB as two additional factors to Perceived ease of use and perceived usefulness in TAM to

formulate C-TPB-TAM model, a model to determine the factors affecting IT usage.

Figure 24 C-TPB-TAM model (adapted from Taylor and Todd, 1995)

Igbaria, Parasuraman and Baroudi (1996) surveyed 471 professionals in North America to

analyze factors affecting the usage of Micro-computers. Study revealed that (1) Perceived

usefulness (2) Perceived fun/enjoyment (3) and Social pressure affects the usage of

microcomputers. Out of these three factors, Perceived usefulness was found to be the most

significant factor affecting adoption. The study also revealed that other less important factors

which affect adoption are (1) Skills (2) Organizational support and (3) Organizational usage.

Kennickell and Kwast (1997). Studied the adoption of electronic banking among

American households in 1995 and observed a positive correlation between role of education

and Consumer skills with adoption of advanced forms of electronic banking for paying bills.

70% of house hold were using electronic banking for direct deposits. Only educated and

skilled customers were using the option to pay bills through electronic banking.

Bruland and Berg (1998) studied the diffusion of textile technology in Norway and came

to the conclusion that diffusion is facilitated by Smooth Technology transfer activities. The

English Textile Technology suppliers facilitated technology transfer by training the

Norwegian works and by supplying skilled British man power as trainers and supervisors.

Hubbard (1998) studied the adoption of onboard information system and found evidence for

the relevance of customer relationship in adoption of innovation.

Mahler and Roger studied (1999) the diffusion of innovation of Telecommunication

technology in banking sector in Germany and concludes that the history of innovation of a

particular organization affects the adoption in present and future. Higher the number of

innovations adoption in the past, more the chances that innovations will be adopted in the

present and future. Mahler and Roger (1999) also pointed out that when there are standards

competing in the industry, the standards will survive if and only if it reaches the critical mass

beyond which the adoption rate increases enormously.

Dasgupta, Agarwal and Gopalakrishnan (1999) posits that (1) Organization factors –

Culture, Size, (2) Environmental Forces – Competition, Competition (3) Market forces –

Computer prices, Exchange rates positively affects adoption of MIS in Indian Manufacturing



firms whereas Role of MIS personnel was found to have a negative impact on adoption. 46

Firms across India were taken for study. Brynjolfsson and Hitt (1995) states that the slow

rates of adoption of IT in organizations in 90s were due to the need for reorganizing their

existing procedures. Organizations were very reluctant to change the ways they operated.

Karahanna, Chervany and Straub, (1999) studied the adoption of Windows technology in

various organizations and put forward the factors affecting “Pre” and “Post” adoption. There

exists difference in the factors affecting adoption of “Prospective adopters” and “Adopters” –

Persons who are likely to adopt and Persons who will continue using the innovation or

readopt innovation. There exists difference in beliefs and attitudes on “Pre” and “Post”

adoption behaviors. Pre- adoption behaviors are generally influenced by “Normative

pressures”, Innovation characteristics, Use fullness, Ease of use, Trialability, Visibility and

Result demonstrability. Post adoption behaviors are influenced by “Instrumentality Beliefs”

of usefulness and Image enhancement.

Kanioviski, Arthur and Ermoleiv (1983) studied adoption in Telecommunication and IT

Industries and emphasized the need for innovations to comply with the “Accepted Standards”.

Any deviation from the accepted stands slows down adoption. Companies targeting to

introduce innovative products should make it a point to confirm to the acceptable standards in

the industry.

Motivations are of two types (1) Intrinsic Motivation (2) Extrinsic Motivation. Both

Intrinsic and Extrinsic Motivation plays important roles in adoption of innovation (Davis,

Bagozzi and Warshaw, 1992). Extrinsic motivation is the motivation to perform certain

behaviors due to its favorable outcomes like better pay, enhanced job performance etc.

Intrinsic Motivation is the motivation to perform certain acts which gives satisfaction or

pleasure. An innovative technology should benefit the customers, at the same time, using the

technology should be an exciting or pleasurable experience. Self-Efficacy refers to a person’s

perception about one’s own talents/ability to execute a particular task or to achieve a goal.

People with higher self-efficacy welcome innovation and lower self-efficacy stymie

innovation due to their lack of belief in their ability to master the innovation (Compeua and

Higgins, 1995). Marakas (1998) coined the term “Computer Self Efficacy” to explain the

individuals’ perception of their own abilities to operate computers at diverse situations.

Eastlick and Lotz (1999) states that the “Intrinsic characteristics” of the customers affects

the adoption of innovation. The adoption intention is affected by three factors namely (1)

Attitude (2) Perceived risk (3) User habits. Attitude is formed by (1) Personal Traits of the

customer (2) Perceived characteristics of innovation and (3) User habits.

Figure 25 Eastlick and Lotz Model (adapted from Eastlick and Lotz, 1999)

To accelerate the speed of adoption, companies should succeed in cultivating a favorable

attitude towards the technology in the minds of customers. The concern regarding the failure


1981-1999)


of the technology in the user’s context should be done away with. Adoption of innovation

requires customers to change their old way of doing things and acquire new ones. The

reluctance on the part of customers should be overcome by convincing them about the

benefits of the technology. The manufacturer should high light the features relevant to

customers to entice them into an act of purchase.

Helper (1995) observed the pattern of diffusion of CNC machines in automotive industry

and posits that adoption is influenced by three factors (1) Expected efficiency gain (2) Market

share of the company (3) Strength of customer relationship. Helper (1995) described

“Customer Relationship” as the most important determinant affecting adoption of innovation.

4. CONCLUSION

Organizations often confront a “Paradox” after innovation adoption. The productivity /

Efficiency will not increase as expected in the short run. In fact it decreases. This concept is

explained by J Curve in adoption. The organization requires time to acquire innovation

specific skill set, infra-structure and new set of systems /procedures. As a result

productivity/efficiency falls in the sort run but in the long run it increases as the organization

get used to new technology (Hornstein and Krusell, 1996). Amara’s law (1983) states that the

customer tends to overestimate the benefits of innovation in the short run and under estimate

the benefits in the long run – which results in wrong technology selections

Innovators have to impart relevant information to the information seeking customers.

Relevant information moves prospect from a state of unawareness to awareness (Wilson,

1981). To be more precise, from the current state to the desired state where a favorable

attitude in incited towards innovation. The processes involved in selection of the relevant

information (which triggers the favorable buying decisions) are namely (1) Starting (2)

Chaining (3) Differentiating (4) Extracting (5) Verifying (6) Ending (Ellis, 1989).

Advertisement enhances the innovativeness of organizations/individuals and a favorable word

of mouth accelerates adoption (Dervin, 1983). Relevant information also reduces or averts the

uncertainties in the minds of prospects regarding performance or features of the product.

Opportunities for Hands on with the innovation reduce the uncertainties or risk associated

with innovation (Lilien, 1990). The innovator should select the appropriate media to impart

information based on adopter characteristics (Daft and Lengel, 1986).

The innovation has to overcome the price hurdle and risk hurdle to get accepted in the

market. The price of innovation should be lower than the maximum price the customer is

willing to pay – reservation price. Income of the adopter/Purchasing power of the adopter also

affects adoption. The affordability of innovation for a particular market should be thoroughly

studied prior to the innovation introduction. (Chatterjee and Eliashberg, 1990). Regarding the

pricing of innovation there are contradictory views. The innovation should be priced

reasonably high at the time of launching as they have first mover advantage and as the time

progresses; the price has to lower to ward off the competition in the market. Contradictory

view is that a lower pricing at the time of product launch attracts customers and as and when

the company gets established in the market, the price can be increased.

In the early stages of adoption, the individuals adopt for economic benefits of innovation

but at later stages the innovation is adopted for want of legitimization – to conform to society

or group pressures. (Tolbert and Zucker, 1983). Industry or Country specific

standards/Government Regulations accelerates adoption for want of compliance (Kanioviski,

1983). Hype cycles of optimism followed by pessimism/Seasonality also affects the adoption

of innovative technologies (Minsky, 1984). The innovation has to delight customers – to

exceed the expectations of the customers. The weightage given to product features varies



from organization/individual/industry to organization/individual/ industry. At times the

innovation simply fails due to the lack of a particular functionality or feature which is

perceived as the most important functionality or feature by organization/individual/industry.

This concept is called Reverse Salient (Huges, 1983).

Individuals make a decision regarding the innovation or any product based on the

“Value” delivered. Customers’ assigns weightage to different product attributes and his

selection is based on the “Highest value” of summation of “Intensity of a particular attribute”

and “Weightage assigned to that attribute” (Roberts and Urban, 1988). Adopters decides on

the innovation based on three rules (1) Max Rule – highest value delivered (2) Mean Rule –

Highest mean value delivered to an adopter set (3) Min Rule – Highest mean value delivered

to an adopter set. The utility of an innovation increases as the number of adopter of the

innovation increases. In fact the value delivered depends on the number of adopters as well.

This is called Positive network externality. Negative network externality refers to decease in

utility as the number of adopters increases (Farrel and Saloner, 1985). Self Confidence of an

adopter to use an innovation impacts the acceptance of innovation (Bandura, 1977).

The rate of adoption is affected by the substitution effects – Replacement purchase of

existing customers and first time purchase of potential adopters affects the rate of adoption

(Kumar and Kumar, 1992). Inter category and Inter generation competitions affects the

adoption of technologies (Kim, Chang and shocker, 1998) and the sales of a particular product

depend on the sales of related innovation. This concept is called Contingent Innovation

(Bayus, 1987). Cost of adoption (complimentary investments required) discourages adopters.

The distribution channels or the partners should be given due importance – should be viewed

as customers itself (Jones and Ritz, 1991). The decrease in price of Prestige goods decreases

the rate of adoption as the price decreases reduces the prestige appeal. This is called SNOB

Effect.

The steps in adoption are explained by various scholars as (1) Imitation -------Adoption----

----Adaptation--------Acceptance---------Performance---------Incorporation (Kwon and Zmud,

1987) (2) Awareness------Information------Personal------ Management------Consequence-------

-Collaboration----------Refocusing (Hall and Hord, 1986) (3) Initiation-----Adoption----

Adaptation-----Acceptance-------Routinisation----Infusion (Cooper and Zmud, 1990).

Various categorizations of adopter sets are done as (1) Non Triers, Tries, Post Trial Non

Repeaters, Post Trial Repeaters (Hahn, 1994) (2) Adopter Sales, Replacement Sales, Multiple

Purchase (Hong and Labe, 1989). (3) Rejecters, Adopters, Disapprovers, Uncommitted

(Sharif and Ramanathan, 1988).

Post 1980s and Pre 2000 period witnessed the shift of focus from process stream of

research to factor stream of research. Very few studies have combined both streams of

research. Diffusion researchers started concentrating more on diffusion from an

Organizational perspective.

The factors affecting adoption can be broadly classified as (1) User community (2)

Organization (3) Technology (4) Task (5) Environment. (Kwon and Zmud, 1987).

The factors from various studies are consolidated as follows

1. Budgets/Credits

2. Inertia

3. Trial

4. Word of mouth/Advertising/Media/Promotions

5. Ease of use/Convenience

6. Safety


1981-1999)


7. Risk/Uncertainty

8. Human Skills/Absorptive capacity

9. Performance/Features/Usefulness/Quality

10. Legitimization/Peer Pressure/Imitation

11. Management Commitment

12. Hype Cycles/Seasonality

13. Cross Functional Teams

14. Network Externality

15. Price

16. Perceived Behavioral Control

17. Industry Standards / Government Regulations / Environmental Friendliness /

Subsidies

18. Organizational Policies/Procedures/Strategic Fit

19. After Sales Service/Warranty

20. Company size/culture

21. Customization/Compatibility/Flexibility

22. Complementary product Sales

23. Income/Purchasing power

24. Attitude

25. Job fit

26. Infrastructure/Facilitating conditions

27. Long Term and Short Term consequences

28. Leadership

29. Subjective norms/Personal Traits of User

30. Reinvention

31. Habits

32. Employee Support

33. Relevant Information

REFERENCES

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[3] Ajzen, I., 1991. The Theory of Planned Behavior. Organizational Behavior and Human

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[4] Amara, R.C., 1983. Business Planning for an Uncertain Future. New York: Pergamon

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