Utilitarian Usage of Social Media for Information Diffusion About

Smart Agricultural System

Berkmans J

, A. Peter

and S. Latha

Commerce, St. Joseph’s College, Tiruchirapalli, India

Alagappa Government Arts College, Karaikudi, India

Keywords: Smart Agricultural System, Information Diffusion, Utilitarian Consumption, Social Media.

Abstract: In this information era people who residing metro politician city required lot of interest to know the benefits

of smart agricultural system adopting in their home. This will influence the farmers in the future. Therefore,

it will impact the whole universe to reduce carbon level, as the consequence of this the temperature would be

in control in the planet. The aim of this research is to know how the social media is being used for information

diffusion about Smart Agricultural System (SAS). This research has adopted a descriptive in nature and

convenient sampling techniques. 219 samples have been employed from the household in cities and metro

politician city. The ANOVA test result’s exposed that the residence of cities and metro city have very good

awareness in adopting SAS but they have ineffective usage of greenhouse related products and further failed

to understand the usefulness of smart agricultural system.

1 INTRODUCTION

Sadhguru (2022) shared in a video conversation that

we receive same level of rain today as we received

before 200 years. But the change is that we have huge

showering within short period which cause

catastrophe on lives, infrastructure, and agriculture

sector. This amount rain is supposed to shower the

whole year with a span of distance. According to him

the reason for the change is due to the increment of

earth temperature year by year. The agricultural

sector is being affected very severely relatively other

sectors. Smart Agriculture involves using

technologies like Internet of Things (IoT), sensors,

location systems, robots, and machine learning on

farms. The primary objective of SAS is to enhance the

overall quality and quantity of crops while optimising

human labour efficiency. In general, Indian farmers

are facing problems with adoption of education,

innovation and technology. This significantly impact

with their productivity and turned into least attractive

business. Social media is the platform where

individuals engage in creating, sharing, and

https://orcid.org/0009-0004-0730-109X

exchanging ideas and knowledge within online

communities and connections. The research

evaluated the use of Facebook, Twitter, Instagram,

LinkedIn, YouTube, and Orkut for utilitarian

purposes. Indian consumers used internet and social

media frequently for hedonic consumption whereas

this research explore that how effectively the social

media is being used for utilitarian purpose. This kind

of consumerism is characterized by a focus on the

practical qualities of products and services and an

effort to satisfy one's material rather than subjective

wants. Increasing user performance or productivity is

one example of an instrumental benefit that utilitarian

systems aim to give. In this connection, the researcher

has proposed that social media has to be used for

information diffusion in implementing SAS.

2 RESEARCH PROBLEMS

From 1750 to 2023 an average earth temperature has

been increased 0.9degree. Due to this we face

recently unpredicted climate changes in Tamil Nadu

J, B., Peter, A. and Latha, S.

Utilitarian Usage of Social Media for Information Diffusion About Smart Agricultural System.

DOI: 10.5220/0012881300004519

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 58-63

ISBN: 978-989-758-714-6

coastal area such as Chennai and Thoothukudi region

as rain showered up to 92cm without predicting it.

ISRO scientist Venkateshwaran explains that if the

level of carbon is increased in the space, due to that

the level of temperature also will be increased

constantly. Further he added that from 1750 our space

carbon level was only 280ppm (Parts per million), in

1999 it was 367ppm, and 2017 it was 421ppm. The

temperature has been increased 1.5times since 1750

on average of less than 200 years. Therefore, there is

a change in the universal cycle which results in

increment in the temperature has the level of carbon

elevated. As social animal human being produces

carbon dioxide whereas the eco-system produces

oxygen. Because of this inefficient cyclical process,

the heat level is increased up to one degree. If the heat

level is increased, the water evaporation also

pointedly increased in the sea. Because of this we

have frequent severe cyclonic storms created at the

sea level. For the last 150 years the rain level has been

increased in Karnataka region whereas Srinagar

region witnessed decreased snowfall related rainfall.

The rain level has been increased Goa and Gujarat

regions but in Kerala region the rain level has been

decreased. Human being failed to understand this and

they wanted to produce more carbon related products

such as auto mobile, industrial products which have

short life in order to generate more profit without

concern of environmental changes. Due to the above-

mentioned causes, there is huge impact in agricultural

sector comparatively other sector products.

Therefore, this research tries to find out the

implementation of Smart Agriculture System and

utilitarian usage of social media in creating

sustainable society (Theekkathirnews, 2023).

3 REVIEW OF LITERATURE

3.1 Awareness

Engaging in social media has a beneficial and notable

impact on the degree to which farmers adopt Low-

carbon Agricultural Practices (LAPs). Engaging in

social media can greatly boost the earnings of farmers

with a lower income bracket. Qi Yang and Wang

(2021) found that social media has a significant

influence in the spread of LAPs among farmers,

which in turn increases economic development in

emerging nations. When farmers share invaluable

information via online communities, the costs of

reducing emissions decrease. Policy benefits for

agricultural adaptation to climate change can be

enhanced by promoting sharing of knowledge and

fostering social development within farming

communities (CordeliaKreft& David Schäfer, 2023).

A greenhouse equipped with a smart gauge yields

superior energy conservation and decreased

emissions outcomes. Leveraging multi-parameter

tracking is advantageous for efficient greenhouse

management, with wireless connectivity increasingly

supplanting traditional connections for data transfer

both within and outside the greenhouse.

Technological advances such as neural learning and

big data are beneficial in greenhouse monitoring,

enhancing autonomous greenhouse management and

optimising energy utilisation in greenhouse building

(Haixia Li & Zhao, 2021). The correlation between

social media reporting and farmers' attitudes on crop

choice, land management, and water storage was

favorable and statistically significant. Social media

reports have a strong influence on farmers' decisions

regarding adopting sophisticated techniques for crop

selection, pest control, land management, and water

storage (Javed, 2023). Federico Platania & Arreola

(2022) propose that social media engagement,

especially by the Federal Emergency Management

Agency (FEMA), can lead to market-related and

societal apprehensions regarding potential shortages

and economic difficulties, thereby causing an

increase in agricultural commodity prices. Increased

public worry about climate change and uncertainties

in economic decisions heighten market response,

enhancing social media influence (Federico Platania

& Arreola, 2022).

3.2 Greenhouse Environment

The Internet of Things (IoT) technologies, including

smart sensors, devices, network structures, big data

analytics, and intelligent decision-making, are seen as

a potential answer to greenhouse farming obstacles

like climate control, crop monitoring, and harvesting

(RakibaRayhana & Zheng Liu, 2020). Using IoT

technologies in smart greenhouses requires balancing

the costs of agricultural output with environmental

protection, ecological degradation, and sustainability.

Implementing IoT infrastructure requires a

significant amount of cash and typically results in

increased energy consumption, which raises the

potential for climate change (Maraveas & Arvanitis,

2022). The GMaaS programme offers forecasts using

computational models created for indoor climate,

agricultural production, and irrigation operations.

Typically, these models are programmed directly into

applications or integrated into software tools for

usage as Decision Support Systems (DSSs). The Web

application utilises the Representational State

Utilitarian Usage of Social Media for Information Diffusion About Smart Agricultural System

Transfer (RESTful) services of the platform to enable

users to easily interact with the system (Muñoz &

Sánchez-Molina, 2022).

3.3 Usefulness

Farmers incur significant financial losses due to

inaccurate weather forecasts and improper watering

techniques for their crops. Advancements in wireless

sensor technologies and miniaturised sensor devices

allow for its utilisation in automatic environment

monitoring and managing greenhouse parameters for

Precision Agriculture (PA) applications (Chaudhary

& Waghmare, 2011). German farmers are cognizant

of climatic shifts and have a collective dedication to

decreasing greenhouse gas emissions, although they

lack adequate knowledge. Without regulation of

agricultural greenhouse gases through taxes or

subsidies, personal motivation remains the most

powerful driver for voluntary emission reduction

(Kerstin Jantke, 2020). Implementing different

technologies to meet these goals was limited by the

absence of a single solution that could immediately

enable agricultural producers to achieve zero energy

use, zero emissions, and optimal resource utilisation.

The agricultural system integrates intelligent

frequency conversion irrigation and automatic

control in greenhouses using sensor nodes, wireless

transmission network, sensor setup, and data

collection system. The technique designed for

practical implementation in greenhouses has shown

positive results. The farmer achieved significant

economic and ecological benefits by automatically

acquiring real-time data on greenhouse environment

parameters and biological information. This has great

importance for the advancement of contemporary

agricultural information-based and intelligent

systems (Guo & Zhong, 2015). Enhanced crop

management and increased crop yields through the

utilisation of our intelligent agriculture technology.

The cost-effectiveness of the system is determined by

the original cost, running costs, and the reliability of

wireless sensor network (WSN) data. Therefore, it

might be utilised for precise crop production planning

and decision-making about cultivation activities

(Denis Pastory Rubanga& Shimada, 2019).

4 METHODOLOGY

This research is conducted to find out that how Smart

Agricultural System is used to impact the carbon

level. It is descriptive research in nature and selecting

greenhouse owners residing in Madurai, Trichy and

Chennai. 219 data were collected through filed visit

and video conferencing method. The convenient

sampling technique was adopted. The secondary data

were collected from different journals, magazines,

you tube channels, reports from state and central

government websites meant for agricultural related

information.

5 DATA ANALYSIS AND

INTERPRETATION

Table 1: Social Media usage and Information Diffusion

About Smart Agricultural System (SAS).

Social Media

Usage

Category

No. of

Respondents

Percent

Social media

frequently

used for SAS

63 29

Instagram 48 22

Facebook 85 38

Linkedin 08 4

Orkuit 05 2

Twitter 10 5

Total 219 100

(Source: Primary Data)

The above table 1 enlightens that the utilitarian

usage of social media for efficient information

diffusion and implementation of smart agricultural

system. 38 percent of the householders are using

Facebook for updating greenhouse product related

information, 29 percent of the greenhouse owners are

using Whatsapp as their communication channel to

understand and update online agricultural market

related activities, 22 percent of the householder used

Instagram, 5 per cent of the respondents are using

Twitter, 4 percent of them are using Linkedin and 2

per cent of them are using Orkuit as the channel for

communication and receive information related to

smart agricultural system. Therefore, it is obvious that

38 percent of them have used Facebook as their

channel for information diffusion about smart

agricultural system.

6 FACTOR ANALYSIS FOR

IDENTIFYING THE SMART

AGRICULTURAL SYSTEM

Factor analysis has been conducted with 24 items that

stimulate the greenhouse owners to implement smart

agricultural system and their purposive use of social

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

media for information diffusion. The principal

component analysis is performed in order to discover

the different factors.

KMO measure of sampling adequacy and

Bartlett’s test of Sphericity values are found out

through performing factor analysis. The result of

KMO measure of sampling adequacy (0.901) exposes

that there is a satisfactory sample size for conducting

factor analysis. It confirms that variables are

correlated and suitable for performing factor analysis.

Principal Component Analysis has been

performed for extracting factors with Eigen value

greater than one. Three latent variables have been

extracted from twenty four observed variables using

the factor analysis and these three factors which were

extracted together accounted for 68.17% of the

variance. The results have been attained through

rotated varimax and these variables with loading

greater than 0.50 were reserved for further analysis.

Three items with loadings below 0.50 were not

considered for further analysis. The percentages of

variance explained by three factors are

17.83%,38.87%and 11.47% respectively. These three

factors are termed as “Awareness”, “Greenhouse

Environment” and “Usefulness”.

6.1 Awareness

Factor one named as “awareness” and accounted 18%

of the total variance such as ‘awareness about IoT

usage’, ‘availability of IT infra’, ‘awareness about e-

banking’ and e-commerce, Expenses ability for

digital marketing, Ready to learn new avenues and

Interested for online marketing related awareness

variables were tested.

6.2 Greenhouse Environment

Greenhouse environment has emerged as an

important factor for smart agricultural system and it

accounted for 39% of the total variance. Nine

variables are loaded in the second factor which are

‘zig bee wireless sensor network technology’ (0.727),

‘radio frequency identification’ (0.697), ‘wireless

sensor network’ (0.694), ‘sensors and global

positioning system’ (0.687), ‘intelliSense internet of

things’ (0.686), ‘computer environment control

system’ (0.686), ‘light detection and ranging’ (0.671),

‘long wave infrared’ (0.652) and ‘internet +’ (0.645)

products used for agricultural environment at their

home residing in metro cities.

6.3 Usefulness

Factor three titled on “Usefulness” and accounted for

11% of the total variances. Six statements are loaded

in the third factor which are ‘affordability for

customers and farmers too’, ‘availability of buyers’,

‘quick response of buyers’, ‘quick order processing

and execution’, ‘less stress of logistics and

warehousing’ and ‘ready to pay premium charges’ for

implementation of smart agricultural system in

household.

Table 2: Cronbach’s Alpha Values for Adoption of Smart

Agricultural System (SAS)

SAS No. of. Items Alpha

Awareness 6 0.888

Greenhouse Environment 9 0.954

Usefulness 6 0.832

(Source: Primary Data)

The reliability table 2reveals the alpha value of

each latent variable with number of observed

variables. The first latent variable termed as

“awareness” comprises six variables with the alpha

value of 0.888.The second factor called as

“greenhouse environment” comprises nine items with

the alpha value of 0.954 and the last factor is termed

as “usefulness” which contains six variables with the

alpha value of 0.832. The above test conformed that

there is an excellent reliability as concerns to this

construct.

: utilitarian usage of social media has a

significant difference with information diffusion

about smart agricultural system.

Table 3: Mean Comparisons of Usage of Social Media and

Smart Agricultural System

Adoption of Smart Agricultural System

Social

Media

Usage

Awareness

Greenhouse

Environment

Usefulness

Mean SD Mean SD Mean SD

Whatsapp 2.01 0.467 2.02 0.646 1.86 0.558

Instagram 2.06 0.535 2.14 0.540 1.61 0.636

Facebook 1.93 0.462 1.87 0.574 1.94 0.429

Linkedin 1.97 0.552 1.97 0.516 1.78 0.514

Orkuit 1.82 0.554 2.10 0.561 1.90 0.588

Twitter 2.36 0.660 2.02 0.607 1.90 0.636

(Source: Primary Data)

Utilitarian Usage of Social Media for Information Diffusion About Smart Agricultural System

Table3 illustrates that mean comparisons of usage

of social media information diffusion and

implementation of smart agricultural model. Twitter

(M=2.36) has utilized frequent channel for promoting

awareness whereas Orkuit (M=1.82)channel has used

least awareness about the smart agricultural system.

Majority of the householder have used Istagram

(M=2.14)as mostly used channel for spreading and

receiving information and Facebook (M=1.87)has

been used least channel for creating and adopting

greenhouse environment. Facebook (M=1.94) has

significantly spread useful information about

greenhouse product whereas Instagram(M=1.61) has

used for least information about of greenhouse

products. Therefore it is concluded that the channel

Twitter (M=2.36) has been frequently used for

information diffusion about smart agricultural

system.

Table 4: One Way Analysis of Variance Summary Table

Comparing Social Media Usage for Information Diffusion

about Smart Agricultural System

SAS Sumof

Squares

Mean

Square

F Sig.

Greenhouse

Environmen

BetweenGroup

2.742 4 0.686

2.05

0.08

WithinGroups 137.21

0.33

Total 139.96

Awareness

BetweenGroup

11.40

4 2.851

9.21

0.00

WithinGroups

127.404

0.309

Total

138.806

Usefulness

BetweenGroup

2.359 4 0.590

2.08

0.08

WithinGroups 116.62

0.28

Total 118.98

(Source: Primary Data)

One-way ANOVA table 4 states the comparison

between purposive usage of social media for

information diffusion about smart agricultural

system. The alternative hypothesis is accepted for

“Awareness” variable whereas effective use of

“Greenhouse Environment” related product and

usefulness variables have been rejected by the

alternative hypothesis. Therefore it is conceded that

greenhouse owners have highly awareness about

greenhouse agricultural systems whereas they don’t

have effective implementation of greenhouse

products and its usefulness to the nature.

7 DISCUSSION

38 percent of the household used Facebook as their

social media platform to promote and shares

information related to smart agricultural system.

Tiwtter is the highly influenced social media in

information diffusion about smart agricultural

system. These households have good awareness of

the information diffusion about the smart agricultural

system whereas they have very least interest or

indifference with effective usage of greenhouse

related products. They have less concern about

usefulness of greenhouse product which has the effect

of climate changes as the consequence they faced

sever cyclone in the past. Hereafter greenhouse

owners should be very cautious to use least cost or

affordable cost greenhouse products at their home for

effective management of soil, climate, and

temperature assessment for cultivation of agricultural

products. Therefore we can avoid facing much losses

in connection with sever cyclone in the metro cities.

Majority of them have education and have financial

stability to take care of our cities for better life.

Therefore we can create sustainable society to donate

to the forthcoming generations.

8 CONCLUSION AND FUTURE

RESEARCH

The technology that was used for SAS would be

environmental friendly. It is, fundamentally a support

and management tool in agricultural sector.

Environmental predictive system is used to predict

the weather condition in advance and the farmers

proactively take certain decision to get better yield

and increasing their productivity. In the recent years,

this predictive system has also been very challenging

for agriculture sector, public infrastructure and

business. This system sometime is used for adequate

management of the agro- ecological parameter of

temperature, relative humidity and luminosity, which

directly benefit to the growth of the crops. The

proposed system (SAS) will create green

environment in the city and minimize the carbon level

around their residential place. It will allow the

householders to take needed preventive and correct

action by providing a technological platform based on

free software and least cost hardware as well as the

data mining techniques.In future, complete success of

this model also will influence the farmers in the near

future. The future direction is howcan be greenhouse

products effectively used by contract farming.

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

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Utilitarian Usage of Social Media for Information Diffusion About Smart Agricultural System