Qualitative Technology Transfer Model for Predicting Adoption of

Information Delivered Through Information and Communication

Technology: A Case of Experimental Research

A. Dharanipriya

1,*

, C. Karthikeyan

and S. Panneerselvam

Department of Agricultural Extension & Rural Sociology, TNAU, Coimbatore, India

Department of Agro Climate Research Centre, TNAU, Coimbatore, India

Keywords: ICTs, Adoption, Prediction, Models.

Abstract: Everett M. Rogers’ Diffusion of innovation model is still ruling the field of agricultural extension research with

respect to technology adoption due to the validity and relevance of the model even in different contexts.

However, the changes in the communication strategy used by the agricultural extension system in the recent

past would have created changes in the stages of adoption of scientific information by farmers. Hence, the

present paper deals with an ICT based technology transfer model proposed to predict the adoption of

agricultural technological information disseminated through smartphones. In the proposed model, adoption

of technological information obtained through ICT tool based agro advisories consisted of six stages such as

awareness, need, knowledge, evaluation, decision, and gratification. The traditional categorization of adopters

is not relevant to this context as ICT based extension services especially in the form of agro advisories deliver

information that has time utility and demands immediate response from the farmer. In such cases, only

innovators who adopt the advisory were considered in the model. This model would be helpful for bureaucrats

and R & D proponents to anticipate the likely rate of adoption of agricultural technologies by farmers and

visualize the impact of ICT based agricultural extension projects.

1 INTRODUCTION

Since decades, several models on adoption of

technologies by farmers have been evolved as an

outcome of research works of eminent scientists in

the field of sociology, psychology, communication,

etc. However, the techniques to estimate the extent of

adoption of new agricultural technologies and

improved practices by farmers are still based on the

principles of Everett M. Rogers (2003) diffusion of

innovations theory which is still ruling the field of

agricultural extension research with respect to

technology adoption due to the validity and relevance

of the model even in different contexts. However, the

changes in the communication strategy used by the

agricultural extension system in the recent past would

have created changes in the stages of adoption of

scientific information by farmers.

Since its inception, the focus of agricultural

extension is to change the knowledge, skill, and

Corresponding author

attitude of farmers by educating them with the

information relevant to the agricultural innovations

developed through scientific research. During the

course of time, different extension communication

methods ranging from individual methods to ICT

enabled mass communication methods have been

evolved so as to better suit the broad spectrum of

contexts brought about by changes in the

demographic, socio, economic and environmental

factors. With the advanced and dynamic growth of

ICTs, how quick are the farmers accepting the

information disseminated through these technologies

depends on several factors. This emphasizes the

importance of studying those factors, re-evaluate the

stages of adoption of technological information by

farmers in the era of information technology

revolution and to reconstruct the orientation of

agricultural extension accordingly.

Dharanipriya, A., Karthikeyan, C. and Panneerselvam, S.

Qualitative Technology Transfer Model for Predicting Adoption of Information Delivered Through Information and Communication Technology: A Case of Experimental Research.

DOI: 10.5220/0012882300004519

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture (ICEISA 2024), pages 86-92

ISBN: 978-989-758-714-6

The research evidence on cyber extension is

bountiful on modelling the factors that leverage

farmers’ adoption of ICT tools for accessing the

details pertaining to the emerging agricultural

technologies and improved practices in the face of

dynamic agriculture (Islam et al,2011; Mittal et al,

2016; Rathod, 2016). Yet, the likely extent of

adoption of information disseminated through ICTs

and the potential benefits upon adoption of improved

agricultural practices cannot be ascertained from

these models. In addition to this, there is a vast body

of scientific literature on the ex-post facto analyses of

adoption and impact of information delivered through

several ICT projects (Ninsiima, 2015;

Harmandeep,2016; Murugan and Karthikeyan,

2017). However, attempts to synthesize this empirical

evidence on the extent of adoption of technologies

into a model to predict the adoption of information

delivered through ICTs by farmers is negligible.

There has been a huge demand for adoption

prediction models among the policy makers and

agencies involved in designing ICT based extension

projects to estimate the likely benefit of the projects

through making assumptions about adoption. Despite

the increasing need, an ex-ante model predicting

farmers’ innovation adoption like the models on the

spread and acceptance of new consumer products

(Bass, 2004) has not been developed for wider use. A

unique model for predicting adoption has not been

developed for a specific context which may be

attributed to the influence of complex social and agro-

ecological factors on adoption outcome variables

(Llewellyn and Brown, 2020).

Hence, the present paper deals with an ICT based

technology transfer model proposed to predict the

adoption of agricultural innovation disseminated

through Information and communication technology.

The proposed model is based on the well-established

diffusion of innovation model with minor

modifications, and it focuses majorly upon the

adoption process and adopter categories.

2 METHODOLOGY

Several factors or constructs that could influence the

acceptance of an innovation, either be an object or

information have been proposed in the models and

theories over the years to explain the user’s adoption

of new technologies. Hence, using the theoretical

framework of different technology adoption models

proposed by various scientists and based on the

survey experiences & cross verified cases through

triangulation, a qualitative model has been proposed

to predict the adoption of information delivered

through Information and communication

technologies. The present research study uses the data

from experimental research where weather based

agro advisories were delivered to the farmers’

smartphones. Medium range weather forecasts have

high accuracy, high to very high usefulness which

influence the decisions of cultivators pertaining to the

routine farm operations (World Meteorological

Organization, 2012) and relevant agro advisory

information is chosen for dissemination through

smartphones to farmers.

The research study was conducted in the Erode

district of Western zone of Tamil Nadu state in India.

A sample of 90 farmers who had access to

smartphones with internet facility, who don’t have

subscription to any other agro advisories being

offered by government and private entities and who

expressed their willingness to receive the advisories

for the ensuing crop season were selected as subjects

for experimentation. Weather based agro advisories

from field preparation to harvest accounting to

sixteen numbers has been disseminated through

WhatsApp for the whole crop season on a weekly

basis to the farmers. The findings on the adoption of

a particular advisory which was found to be new to

the farmers were considered to arrive implications

about the adopter categories for the proposed model.

3 FINDINGS AND DISCUSSION

As it was recognized by the early researchers,

adoption of information consisted of stages. It is not

the result of a single decision to act but series of

actions and thought decisions (Wilkening, 1953). In

the proposed model, adoption of technological

information obtained through ICT consisted of six

stages viz., (i) awareness, (ii) need, (iii) knowledge,

(iv) evaluation, (v) decision and (vi) gratification.

The ICT based technology transfer model is

depicted in Figure 1.

Figure 1: ICT based technology transfer model.

Qualitative Technology Transfer Model for Predicting Adoption of Information Delivered Through Information and Communication

Technology: A Case of Experimental Research

3.1 Awareness Stage

Awareness is the key to adoption, which is decided

through acquaintances of the farmers with the

technological information. Adoption of information

related to innovation requires farmers to be aware of

the existence of innovation. According to the model

of diffusion of innovations, we cannot potentially

expect the farmers to accept an innovation until they

know about it. Awareness and positive attitude

toward innovation come from information about

innovation (Napier et al., 2000).

Specific to the present context, a farmer becomes

aware of the technological information by

acquaintances with their access to ICT tools. Now-a-

days, several ICT based technology transfer projects

have been implemented by the change agency system

(Public extension departments, private and corporate

sector) to create awareness among farmers to

motivate them to adopt the information. Though an

individual is aware of the existence of innovation

from the ICT tools, they consider the innovation for

adoption only when they recognize the need for

application of the obtained information. This states

that awareness is followed by the need to make

decisions on utilization of the information. Hence,

awareness is a passive activity. The decision to use

the information stops with the awareness stage when

the need for it is not felt. Hence, it is proposed that

awareness precedes need. However, this is

contradictory to Singh (1965) who argued that need

precedes awareness. But an individual develops a

need when he or she learns that an innovation exists.

This is in coherence with (Rogers, 2003), who

indicated that awareness about innovations can lead

to needs and vice versa. So far, research has not

provided an accurate answer to the question “Does

awareness precedes need or need precedes

awareness?” This has been addressed in the proposed

model stating that awareness precedes need.

Hence, awareness is proposed as the first stage of

adoption of information delivered through mobile

enabled extension services.

3.2 Need Stage

From the observations of the study, it was found that

farmers seldom develop interests to seek further

details of the delivered information, unless they feel

the need for the information. Rogers (2003) reported

that awareness knowledge about the existence of

innovation may motivate an individual to seek further

information i.e., “how to” knowledge to proceed with

the innovation decision process. He further added that

felt needs/problems as the prior conditions for

knowledge. In the present context, it was observed

that farmers receiving mobile based agro advisories

gave significant attention to information that is in

high demand for them. For instance, it was evident

from several research that farmers accord more value

to the information on market price, weather forecasts,

forecasts on pest and diseases outbreak and schemes/

subsidies as they felt the need for such information

and the information being considerably new to their

level of knowledge. All the other information on crop

cultivation practices has not gained considerable

attention from the farmers as its need has not been felt

by most farmers. Hence, it is proposed that farmers

give attention to the advisories only when they feel

the need. This is in congruence with Hassinger (1959)

who argued that even if individuals are exposed to

innovation messages, such exposure will have little

effect unless the innovation is perceived as relevant

to the individuals’ needs. Hence, need has been

proposed as the second stage of adoption of

information disseminated through information and

communication technologies with special reference to

mobile enabled agro advisories.

3.3 Knowledge Stage

Knowledge is proposed to be the third stage of

adoption of information disseminated through ICT

tools. Knowledge here refers to the understanding i.e.,

constructing meaning from the instructional

messages. It includes interpretation of the information

by farmers. Knowledge is regarded as the initial stage

of cognitive process that corresponds to transfer of

information from understanding to application in the

field to solve the problems. At this stage, the

disseminated information will become the

knowledge. Rogers (2003) represented three levels of

knowledge viz., awareness-knowledge about the

existence of the innovation, “how-to” knowledge

about the information necessary to use the innovation

properly and principles-knowledge about the

information dealing with the functioning principles

underlying how an innovation works. He stated that

behaviour of individuals seeking information on

“how-to” knowledge and principles-knowledge is

concentrated at the knowledge stage of the innovation

decision process, though it may also be found to occur

at the persuasion and decision stages. The proposed

model derives correspondence from Rogers’ (2003)

diffusion of innovation model.

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

3.4 Mental Evaluation Stage

At this mental evaluation stage of adoption of

information delivered through ICTs, the individuals

make subjective judgments on the appropriateness of

information delivered regarding the characteristics of

the message such as timeliness, credibility, relevance,

and adequacy based on their experience in farming.

These attributes of the information are important at

this stage to motivate the individuals to evaluate the

usefulness of the information delivered through ICTs.

However, based on the observations from the

study, credibility and relevance of the messages were

found to have a crucial influence on the individuals’

decision to adopt the delivered information. This was

due to the anonymity of the source of information

delivered through ICT’s. As in the present scenario,

different stakeholders of agricultural extension

service delivery are involved in dissemination of agro

advisories to the farmers, it increases the degree of

uncertainty about the technological information for

the farmers who were typically not certain about the

utility of the information.

Here comes the need of interpersonal

communication networks to reinforce the delivered

information. This was reported based on the

observation that farmers in the study area discussed

the information obtained through ICTs with their

fellow farmers and agricultural extension officers to

consider the information for adoption. Information

networks have a major influence in the evaluation

stage to motivate farmers to pass on to the decision

stage, though it might influence the farmers’ decision

at the decision stage.

It is proposed that ICTs play an important role

only upto the stage of mental evaluation. To motivate

individuals to decide to adopt the information, their

information communication networks play a

distinctive and prominent role.

It is proposed that the stages of adoption of

information delivered through information and

communication technology ends at the mental

evaluation stage itself for adopter categories as

proposed by Rogers (2003) with the only exception to

the innovators who adopt the information.

3.5 Decision Stage

Farmers who perceive the information disseminated

through ICTs to be appropriate for their field

conditions will make decisions on adoption based on

factors such as outcome expectations, facilitating

conditions and social influence.

In the proposed model, outcome expectation

refers to the belief about the likelihood that a

particular behaviour would lead to a specific

outcome. If the farmer believes to obtain more yield

upon adoption of information, he will go for adoption.

The proposed model is congruent with the Social

Cognitive Theory model as proposed by Bandura

(1986) in which the construct ‘outcome expectations’

have been used to predict the information technology

usage.

Facilitating conditions is operationalized as the

availability of resources such as farm inputs, labour

and capital to make use of the information

disseminated through agro advisories. This is in

congruence with the Theory of Planned Behaviour as

proposed by Ajzen (1985) which indicated that

planned behavioural control is determined by the

availability of resources, opportunities, and skills, as

well as the perceived significance of those resources,

opportunities, and skills to achieve outcomes. In

addition to this, the proposed model derives support

from the economic constraint model which assumes

adoption as the ability to use innovation. According

to this model, adopters lack the ability to accept new

technologies due to the paucity of funds to use. The

model stated the access to land and capital as the most

important factors limiting the adoption of innovations

(Napier et al., 2000). Social influence is

operationalized as the change in behaviour of an

individual as expected by the other individual. A

farmer’s adoption of information is influenced by

another farmer’s decision due to many factors such as

convenience of carrying out agronomic practices,

market availability etc. This is because the present

study is concerned with paddy crops where

topographic factors have a major influence because of

the slope of land. For instance, if farmers choose to

cultivate a particular variety of paddy, he must seek

the decision of the neighbour farmer to carry out the

farm operations in a convenient way. Hence, farmers

must depend upon other farmers to perform crop

management practices. This derives correspondence

from the Unified Theory of Acceptance and Use of

Technology according to which the constructs ‘social

influence’ and ‘facilitating conditions’ were found to

have significant influence on the user acceptance of a

technology. If the above mentioned three factors are

favourable, then the farmer goes for adoption of

information or else he will not utilize the information

besides being appropriate to them and the same is

validated through the data from experimental

research.

Qualitative Technology Transfer Model for Predicting Adoption of Information Delivered Through Information and Communication

Technology: A Case of Experimental Research

3.6 Evidence from Research

The research results indicated that there was a

significant difference in adoption of information

between the farmers exposed to weather based agro

advisories and the farmers who were not exposed to

such advisories. Although most of the information

disseminated was not new to the farmers, they

continued to adopt the information when it was

reinforced by the advisories disseminated through

smartphones. This could be attributed to two reasons

such as the delivered information being compatible to

their past experiences and the credibility accorded to

the source of information as farmers were informed

about it prior to the dissemination of weather based

agro advisories. However, among the advisories

disseminated, the technological information related to

the use of Biocontrol agents to manage leaf folder and

stem borer attacks in Paddy was entirely new to

farmers and this also had contributed to the

differential adoption rates between the farmers

exposed to advisories and those devoid of advisories.

So, this technological information was considered to

find out the rate of adoption of information

disseminated through smartphones because of the

newness of the information being delivered to the

farmers. The finding on adoption of technological

information related to the use of Biocontrol agents is

given in Table1.

Table 1. Distribution of respondents based on the adoption

of technological information on Biocontrol agent.

S.No. Category Number Per cent

1. Adopters 5 5.56

2. Non adopters 85 94.44

Total 90 100.00

It was found from Table 1 that 5.56 per cent of the

farmers who received weather based agro advisories

had adopted the information within a short period of

time. These farmers are considered as the innovators

as they have adopted the innovation immediately

when it comes to their knowledge. Hence, the study

indicated that the percent of innovators is 5.56 per

cent which is contradictory to the findings of Rogers

(2003) who declared the percent of innovators to be

2.5 %. It was during 1962 that Rogers proved the

innovators to be 2.5 % and by the time now, the

finding has become obsolete. The study inferred that

the adoption of technological information

disseminated through information communication

technology especially smartphones, is only confined

to the innovators and the categorization of adopters

such as early adopters, early majority, late majority

and laggards who adopt the advisory, only after

getting convinced of the consequences faced by

innovators over a period of time were not appropriate

to the context since the advisory delivered was based

on medium range weather forecast which requires

immediate adoption within a week time else the

advisory would become obsolete. Information

delivery through smartphones works on the concept

of avoiding repetition and delivers something new as

and when the farmers gain access to it. This demands

immediate response to the information delivered,

making innovators as the only category to adopt it.

All other categories naturally tend to become non-

adopters in this case.

3.7 Characteristics of Innovators

Based on the observations from the study, it was

found that Innovators were usually educated, large

landholding farmers getting considerable income, and

they were keenly interested in attempting new things

for self-satisfaction.

Hence, use of information is the fifth stage of

adoption of information delivered through ICTs.

However, this stage is not the end. It is followed by

the stage of gratification.

3.8 Gratification Stage

Use of information is not the final stage of adoption.

This is followed by the gratification stage because an

individual continues to access and use the information

delivered through ICTs only if he or she derives

satisfaction out of utilizing it or else, an individual

may revert to the conventional information sources to

cater to their needs. Hence, gratification has been

proposed to be important for persistent adoption of

information delivered through ICTs. This is in

coherence with Ruggiero (2000) who argued that any

attempt to speculate on the future direction of mass

communication theory must seriously include the

uses and gratification theory which intended to

explore why people become involved in one

particular type of mediated communication or another

and what gratifications do they receive from it?

Hence, it is important to study the gratification that

holds the farmers in the use of ICTs and the content

that satisfies their needs. In the present study,

gratification was measured in terms of farmers’

perception on the effectiveness of content, treatment

ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu

of the advisories and the channel used for the

dissemination of weather based agro advisories. The

study revealed that farmers are satisfied with the

weather based agro advisories which was reflected in

their willingness to receive weather based agro

advisories for the following crop seasons.

4 CONCLUSIONS

To sum up, the stages of adoption of information

delivered through Information and Communication

technologies (Smartphone), starts with the awareness

stage and ends with the evaluation stage. Hence it is

proposed that the role of ICTs in influencing the

adoption- decision of farmers terminates in the

evaluation stage. The traditional categorization of

adopters is not relevant to this context as ICT based

extension services especially in the form of agro

advisories deliver information that has time utility.

Such kind of information requires immediate

response from the farmer as it might become obsolete

in future course of time. Hence, only innovators can

adopt the technical advisories delivered through

ICTs. 5.56 per cent of the farmers come under the

innovator category, contradictory to Rogers (2003)

who stated that 2.5 per cent constitute the innovator

category.

The implications of the proposed model for the

policy makers and those engaged in research and

development of ICT based extension strategies are as

follows:

● The proposed model will provide insights

into the adoption and diffusion of

information among farmers in the context of

ICT enabled extension.

● The model will create a greater

understanding of the adoption process for

those involved in designing ICT based

agricultural extension projects which may be

employed to expand the use of innovative

agricultural information delivered through

ICTs.

● Policy makers and R & D proponents may

rely on this model to anticipate the likely rate

of adoption of agricultural technologies by

farmers and visualize the impact of ICT

based agricultural extension projects.

● This model would help the extension

officials to modify their extension strategy

based on the socio economic and agro-

ecological context to achieve the estimated

rate of adoption of agricultural innovations

disseminated through cyberspace by the

farming community.

● Based on the observations and findings from

the research, a qualitative model for

predicting the adoption of information

delivered through ICTs has been proposed.

Future studies may be undertaken by the

extension researchers to test the validity of

the proposed model.

● As the study was conducted at micro level,

further studies can be taken up at macro level

by taking some other technologies to

reconfirm the proposed model.

CONFLICT OF INTEREST

There is no conflict of interest.

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ICEISA 2024 - International Conference on ‘Emerging Innovations for Sustainable Agriculture: Leveraging the potential of Digital

Innovations by the Farmers, Agri-tech Startups and Agribusiness Enterprises in Agricu