Factors Influencing the Use of Uzhavan App by the Farmers

Atsu Frank Yayra Ihou

1,*

, Madhumithra M

, Paul Mansingh J

and Gayathri A

Department of Agricultural Extension and Economics, Vellore Institute of Technology, Vellore, Tamil Nadu, India

VIT School of Agricultural Innovations and Advanced Learning (VAIAL), Vellore Institute of Technology,

Vellore, Tamil Nadu, India

Keywords: Agricultural Advisory Service, Digital Agricultural Innovation, Mobile App

Abstract: The Government of Tamil Nadu, with an equivalent objective of engagement in agriculture 5.0, has devised

the "Uzhavan" mobile app as an additional digital advisory service in farming. The factors influencing the use

of the Uzhavan app were analyzed in this study employing the Unified Theory of Acceptance and Use of

Technology (UTAUT). Data were obtained from 263 farmers in Tamil Nadu. A mixed research method was

used. Quantitative data on factors influencing the use of the app was analyzed using ADANCO software and

thematic analysis was used to study the reasons for non-usage of the app using Nvivo 13. The result shows

that, in direct effect, the effort expectancy and performance expectancy influence the farmer’s behavioral

intention significantly; moreover, the behavioral intention has a positive and significant influence on the use

of the Uzhavan app by farmers. The facilitating condition has a positive and significant influence on the

Uzhavan app use behavior. For the indirect effect, effort expectancy and performance expectancy positively

influence use behavior through behavioral intention but are not significant. The thematic analysis revealed

that non-user farmers were unaware of the Uzhavan app. and lack of knowledge about it.

1 INTRODUCTION

The advents of technology and advancements in

communication have led to the digitalization of

various sectors, including agriculture. This digital

transformation has empowered farmers with easy

access to information through advanced technologies.

However, amid abundant information sources,

farmers face difficulty in gathering accurate and

reliable information (Kumar et al., 2023). With the

widespread availability of smartphones and internet

connectivity, mobile agricultural applications have

emerged as a powerful tool for the advancement and

transformation of the farming sector in both

developed and developing nations (FAO, 2019).

Mobile agricultural applications, as stated by

Costopoulou et al., (2016), have the potential to boost

the income levels of small-scale farmers by reducing

the expenses related to supply and distribution, and

enhancing traceability. Among the plethora of digital

tools aiming to help farmers, the Uzhavan app,

developed by the Tamil Nadu government provides a

ground-breaking initiative of information to farmers

Corresponding author

with easily accessible information. The app provides

a diverse range of features, including weather

updates, seed and fertilizer inventory, the market

price of commodities, valuable crop advisory,

government schemes, subsidies, etc. Utilization of

these features empowers farmers to make decisions

that positively impact their agricultural practices. The

installation process of these applications on

smartphones must be made effortless.

Comprehending the determinants that impact farmers'

decisions to adopt, can enhance their ability to

forecast and elucidate their utilization, thus providing

valuable insights for future development endeavors

(Michels & Mußhoff, 2020). Furthermore, their lack

of familiarity with the technology and absence of

reliable information contribute to their reluctance to

embrace digital tools (FAO, 2019). Thus, this paper

investigates the effective utilization of the Uzhavan

app among farmers, employing the UTAUT theory as

a guiding framework. Through the examination of

factors influencing farmers’ acceptance and use

behavior towards the Uzhavan app, this study aims to

provide valuable insights to enhance the functionality

Ihou, A. F. Y., M, M., J, P. M. and A, G.

Factors Inﬂuencing the Use of Uzhavan App by the Farmers.

DOI: 10.5220/0012889000004519

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 189-195

ISBN: 978-989-758-714-6

189

and outreach of digital tools in agriculture, ultimately

contributing to the sustainable growth and prosperity

of the agricultural sector in India.

2 LITERATURE REVIEW

UTATUT Theory

Model adoption, also known as the Unified Theory of

Acceptance and Use of Technology (UTAUT),

pertains to the process of embracing a particular

model or framework. The research conducted on

model development aims to pinpoint the key factors

that influence the adoption of technology. The

primary goal of this development is to expedite the

acceptance and integration of innovative

technological advancements (Arifin et al., 2022). The

UTAUT model has four main variables: performance

expectancy, effort expectancy, social influence, and

facilitating conditions. Performance expectancy is

about how much individuals believe that using

technology will help them in their work. Effort

expectancy is about how much effort and difficulty

individuals think they will face when adopting

technology. Social influence is about how much

individuals are influenced by others when adopting

new technology, especially by close groups, familiar

people, and industry experts. Currently, the UTAUT

theoretical framework is extensively employed by

scholars as the foundation for developing research

models in various related studies. For instance, it has

been utilized to examine the willingness and

acceptance behavior of farmers towards digital

information technology systems, as well as the

acceptance behavior of new agricultural technologies.

(Lin et al., 2023).

Uzhavan application

As agriculture moves to the digital era, it is crucial to

study mobile applications in agricultural extension

services. According to (Aker, 2011; Labarta et al.,

2011) The inclusion of digital advances in the

agricultural sector, such as the utilization of mobile

applications, has attracted considerable interest as a

catalyst for enhancing agricultural extension services.

Mobile applications provide information, that

promotes effective communication, and decision-

making processes for farmers. The Uzhavan app is a

significant application in the utilization of technology

for agricultural extension. it provides a range of

services to Tamil Nadu farmers that aim to help them

with relevant information (Uzhavan Mobile

Application, n.d.). Initial research conducted on the

Uzhavan app indicates favorable results regarding its

influence on farmers. The app plays a significant role

in strengthening crop management techniques, and

production, and adds to profitability for farmers in

Tamil Nadu. The app has a user-friendly interface and

localized content helps farmers in bridging the digital

gap. The acceptance and adoption of any

technological intervention in agriculture play a

crucial role in determining its success. Extensive

research conducted in the domain of agricultural

extension services emphasizes the significance of

comprehending the factors that impact farmers'

acceptance of technology (Davis et al., n.d.; Labarta

et al., 2011). The Uzhavan app exhibits potential,

however, it is crucial to delve into the challenges and

opportunities linked to its implementation. Previous

research on similar apps has highlighted obstacles

like connectivity problems, differing levels of digital

literacy among farmers, and the necessity for

continuous technical assistance (Labarta et al., 2011;

Qiang et al., 2011). Overcoming these challenges is

vital to optimize the Uzhavan app's impact and

guarantee its long-term effectiveness in the realm of

agricultural extension services.

3 MATERIALS AND METHODS

To examine the factors that impact the utilization of

the Uzhavan app among farmers, an interview

schedule was developed utilizing established scales

derived from the Unified Theory of Acceptance and

Use of Technology. The respondents' perceptions

regarding the credibility of services, performance

expectancy, effort expectancy, social influence,

facilitating condition, behavioral intention, and use

behavior were assessed using a 5-point scale

developed by Liñán and Chen (2009). This scale

ranges from 1 (strongly disagree) to 5 (strongly

agree). The data were collected through personal

interviews conducted with farmers residing in the

Vellore and Ranipet districts in Tamil Nadu State

from November 2023 to January 2024.

For data collection, the research employed a sample

of 263 farmers residing in two districts of Tamil

Nadu. The data was gathered from farmers residing in

4 blocks from Ranipet (121 Responses) and 4 blocks

from the Vellore (142 Responses) districts. The

interview method was opted for to ascertain the

farmers' perspectives while maintaining their

anonymity.

Concerning analysis, first, a confirmatory factor

analysis (CFA) model was employed to analyze the

relationship between the indicators and constructs.

Secondly, a structural path model (SM) was utilized

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

190

to comprehend the relationship among all the

constructs used in the model using ADANCO 2.3.2

Software. A qualitative approach was done through

thematic analysis in Nvivo 13.

4 RESULTS

The data obtained from the original investigation

were examined by the ADANCO 2.3.2 software tool

conceived to execute analyses involving structural

equation modeling (SEM). The research framework

was established and the hypothesis was evaluated

using the ADANCO methodology (Ringle, C. M.,

Wende, S., & Will, 2005).

Measurement model

To obtain accurate inferences, it is essential to

evaluate the reliability and validity of the data. In the

domain of indicator reliability, there is a widely

acknowledged consensus that observed variables

exhibiting an outer loading of 0.7 or higher are

considered acceptable (Hair et al., 2011).

Additionally, the assessment of the internal

consistency and reliability of the scale is carried out

by utilizing statistical measures such as Cronbach's

alpha and composite reliability. Composite reliability

is widely recognized as a more reliable indicator of

internal consistency due to its capacity to uphold the

standardized loadings of the observed variables

(Fornell & Larcker F., 1981). Data validity evaluation

entailed analyzing the average variance extracted

(AVE) to determine convergent validity. According to

Fornell & Larcker F (1981), Items within a measuring

model are deemed to possess satisfactory

unidimensional value when their Average Variance

Extracted (AVE) exceeds the threshold of 0.5.

Table 1 presents the values for the outer loadings,

which range from 0.7402 to 0.8819. These values

indicate a robust association between the indicator

and the construct.

Source: Authors compilations (2024)

Figure 1. The measurement model

Furthermore, it is noteworthy that the Cronbach's

alpha value exceeds 0.7 (Ponterotto, 2007). The

composite reliability values should exhibit a

consistent pattern of being above the threshold of 0.70

(Hair et al., 2011). Hence, the observed factor

loadings, Cronbach's alpha coefficient, and composite

reliability analysis collectively indicate that the scales

employed in the study possess satisfactory reliability.

In the interim, it is observed that all dimensions

exhibit a satisfactory level of average variance

extracted (AVE) > 0.5 (BI= 0.5435, EE = 0.7057, PE=

0.7926, FC = 0.8489 and SI=0.8257). This finding

suggests that the variability in the observed variables

can be attributed to the latent constructs.

Table 2. Discriminant validity

Construct BI EE PE SI FC

BI 0.90

EE 0.47 0.82

PE 0.22 0.04 0.79

SI 0.16 0.12 0.28 0.82

FC 0.20 0.20 0.16 0.03 0.84

Source: Authors compilations (2024)

In addition, the (Fornell & Larcker F., 1981), criterion

was utilized to assess the existence of discriminant

validity among the latent variables in the model. To

ascertain discriminant validity according to the

Fornell-Larcker criterion, it is necessary for the

square root of the average variance extracted (AVE)

for each construct to exceed the correlation with any

other construct within the framework (Fornell &

Larcker F., 1981). The results presented in Table 2

demonstrate that the assessment of discriminant

validity yielded favorable outcomes, indicating

strong construct validity and reliability. In this study,

multicollinearity was assessed using the indicator,

variance inflation factor (VIF). Table 1 reveals that

the indicators in our regression model exhibit

Variance Inflation Factor (VIF) values below the

threshold of 5 (Kock, 2015), demonstrating the lack

of substantial multicollinearity. This enables us to

assertively interpret the distinct impacts of the

predictors on the dependent variable and substantiates

the dependability of our regression analysis.

Factors Inﬂuencing the Use of Uzhavan App by the Farmers

191

Table 1. Measurement model: loadings, composite

reliability, and convergent validity

Source: Authors compilations (2024)

Path Analysis

The analysis reveals the direct impact of the latent

variable on the emerging variable. Initially, the

positive and substantial influence of effort expectancy

(β=0.573; p=0.008) in utilizing the Uzhavan

application is observed in the behavioral intention of

farmers. Furthermore, the performance expectancy

(β=0.309; p=0.049) of the Uzhavan app, significantly

affects the behavioral intention. Additionally, the

behavioral intention (β=0.2477; p=0.087) influences

the use behavior of farmers. Subsequently, the

facilitation condition (β=0.539; p=0.045)

significantly impacts the use behavior of the Uzhavan

app by the farmers. As for the indirect effects, effort

expectancy, performance expectancy, and social

influence do not significantly affect the farmers' use

behavior of the Uzhavan app. This demonstrates that

the farmers' behavioral intention fully mediates

between the three variables and the use behavior.

Table 3. The structural equation model

Source: Authors compilations (2024)

Figure 2. The path model estimation

Thematic analysis

The qualitative data were analyzed using Nvivo

software. The first step was to classify the data, code

each response, and then process of visualization of

themes.

This study presented a word map, coding distribution

by districts and main occupations of the farmers.

Source: Authors compilations (2024)

Figure 3: The reasons for non-use of the Uzhavan app

The map in Figure 3 presents a color-coded matrix of

reasons why farmers may not use the Uzhavan app.

Major factors include a lack of awareness about the

app, an inability to operate smartphones, and the use

of basic mobile phones. Other notable reasons were

the absence of a phone, engaging in agriculture as a

secondary occupation, time constraints, and financial

limitations. The matrix also suggests that a lack of

interest and other unspecified barriers contribute to

the underutilization of the app among farmers.

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

192

Source: Authors compilations (2024)

Figure 4: The district-wise distribution of reasons for not

using the app

Figure 4 displays the district-wise distribution of

reasons for not using the Uzhavan app among

farmers. The graph indicates that in the Ranipet

district, the predominant reason is a lack of

awareness, while in Vellore, the main issue is the

absence of smartphones. Other reasons such as the

use of basic mobile phones and the inability to operate

smartphones are also represented, with varying

degrees of prevalence across the districts. The

category "Agriculture secondary" suggests that in the

Ranipet district, farming is not the primary

occupation, which may influence app usage.

Source: Authors compilations (2024)

Figure 5: The farmer category-wise distribution of reasons

for not using the app

Figure 5 illustrates the percentage coverage of

individuals for whom agriculture is the main

occupation and their reason for not using Uzhavan. It

is observed that farmers for whom the agriculture is

main occupation are more unaware than the second

group of farmers. This can be explained by the

educational level which allows the second group to

have additional activities. The next reason was the

lack of access to smartphones which is largely

observed in the farmers who have agriculture as

primary activities. This might be due to the easy

accessibility of smartphones through the additional

income earned by the second group of farmers from

activities other than agriculture.

5 DISCUSSION

This investigation examines the variables that impact

the utilization behavior of farmers. The study reveals

that the farmer's behavioral intention on the Uzhavan

app is positively influenced by effort expectancy.

Furthermore, the farmers' utilization of this app is

characterized by low effort expended. Consequently,

the utilization behavior of farmers is significantly

influenced by performance expectancy. This can be

attributed to the app's provision of pertinent and

accurate information, daily market prices, and

weather forecasts. However, the study finds that

social influence does not have a significant impact on

farmers' behavior. These findings are consistent with

the research conducted by Sabbagh & Gutierrez

(2022), which demonstrates that only effort

expectancy and performance expectancy significantly

influence the farmer's behavioral intention toward

micro irrigation devices. Moreover, the study

establishes that the farmer's behavioral intention fully

mediates the relationship between effort expectancy

and performance expectancy, and the utilization

behavior of Uzhavan. Similar results were obtained

by Omar et al. (2022), who found that the farmer's

behavioral intention plays a mediating role in the

utilization behavior of E-Agri finance apps by the

farmers. However, the research conducted by Sun et

al. (2021), suggested that effort expectancy,

performance expectancy, and social influence directly

impact the use behavior of the Internet of things. The

social influence did not affect behavioral intention.

The same result was found by Michels & Musshoff

(2020) reported a non-significant relationship

between social influence and farmers’ behavioral

intention to use the agri app in plant protection. The

improvement of the use of the agri app can be done to

make it easier to use, since the effort expectancy

shows a positive influence, and then update regularly

the app. Coming to the non-users of the Uzhavan app,

the main reason is the lack of awareness of the app by

farmers who have agriculture as their primary

activity, and most of them don’t have access to

smartphones. By facilitating access to smartphones

the use behavior of the Uzhavan app can be improved.

The farmers can be assisted through short videos on

the advantages and methods of using the Uzhavan app

as suggested by Kumar et al., (2023).

Factors Inﬂuencing the Use of Uzhavan App by the Farmers

193

6 CONCLUSIONS

This research study uncovers that farmers who utilize

the Uzhavan app generally hold a positive perception

of it. This positive perception stems from the farmers'

belief in the app's user-friendliness and their

expectations regarding its performance, which

significantly influence their intention to use the app.

Moreover, the availability of necessary conditions

plays a crucial role in determining the actual usage of

the app by farmers. However, it is worth noting from

the qualitative analysis that the presence of

facilitating conditions directly impacts farmers' usage

behavior, and it was identified that some farmers do

not possess smartphones; Consequently, access to

smartphones would enable them to fully utilize the

functionalities offered by the Uzhavan app.

Additionally, through qualitative analysis, it was

observed that certain farmers remain unaware of the

Uzhavan app services, lack of access to smartphones,

and lack of knowledge to operate it. To address this

issue, it is recommended to develop concise videos

that can be easily shared through platforms like

WhatsApp and YouTube, thereby reaching a larger

number of farmers to promote awareness. By

understanding the importance of digital access and

the functionalities of the e-Agriextension platform, all

farmers can benefit from the technological

advancements in agriculture.

As a recommendation, extension officials can

strategize awareness initiatives supplemented with

training sessions for farmers. Based on the findings,

it is recommended that to enhance farmers' effort

expectancy, the Uzhavan app should be rendered

more comprehensible, with supplementary audio

attributes for the farmers who are unable to read.

Further investigation can be conducted on the

extent of influence of the digital tools in agriculture,

especially the influence of the use of Uzhavan app on

farmers' economic and profit efficiency.

REFERENCES

Aker, J. C. (2011). Dial “A” for agriculture: A review of

information and communication technologies for

agricultural extension in developing countries.

Agricultural Economics, 42(6), 631–647.

https://doi.org/10.1111/j.1574-0862.2011.00545.x

Arifin, Z., Anggoro, S., Irianto, H., & Purnaweni, H. (2022).

A Systematic Literature Review : UTAUT Model

Research for Green Farmer Adoption. 12(6).

Costopoulou, C., Ntaliani, M., & Karetsos, S. (2016).

Studying Mobile Apps for Agriculture. IOSR Journal of

Mobile Computing & Application, 3(6), 1–6.

https://doi.org/10.9790/0050-03064449

Davis, K., Ngwenya, B., & Chen, L. (n.d.). ICTs and rural

development: Review of the literature, current

interventions and opportunities for action. International

Journal of Communication.

FAO. (2019). Digital Technologies in agriculture and rural

areas.

Fornell, C., & Larcker F., D. (1981). CLAES FORNELL

AND DAVID F. LARCKER* Evaluating Structural

Equation Models with Unobservable Variables and

Measurement Error. Journal of Marketing Research,

XVIII(February), 39–50.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM:

Indeed a silver bullet. Journal of Marketing Theory and

Practice, 19(2), 139–152.

https://doi.org/10.2753/MTP1069-6679190202

Kock, N. (2015). Common method bias in PLS-SEM: A full

collinearity assessment approach. International Journal

of E-Collaboration, 11(4), 1–10.

https://doi.org/10.4018/ijec.2015100101

Kumar, A., Saini, P., & Prakash, A. (2023). Design and

Development of Smart Agri app. International Research

Journal of Modernization in Engineering Technology

and Science, 5(5), 242–244.

https://doi.org/10.17148/ijarcce.2019.8246

Labarta, R., Campos, B., Camacho, G., & López, M. (2011).

Mobile phones and rural livelihoods: Adoption, use,

and impact. Development in Practice. 21(6), 802–812.

Lin, D., Fu, B., Xie, K., Zheng, W., Chang, L., & Lin, J.

(2023). Research on the Improvement of Digital

Literacy for Moderately Scaled Tea Farmers under the

Background of Digital Intelligence Empowerment.

Agriculture (Switzerland), 13(10).

https://doi.org/10.3390/agriculture13101859

Michels, M., Bonke, V., & Musshoff, O. (2020).

Understanding the adoption of smartphone apps in crop

protection. Precision Agriculture, 21(6), 1209–1226.

https://doi.org/10.1007/s11119-020-09715-5

Michels, M., Bonke, V., & Mußhoff, O. (2020).

Understanding the adoption of crop protection

smartphone apps: An application of the Unified Theory

of Acceptance and Use of Technology.

Omar, Q., Yap, C. S., Ho, P. L., & Keling, W. (2022).

Predictors of behavioral intention to adopt e-

AgriFinance app among the farmers in Sarawak,

Malaysia. British Food Journal, 124(1), 239–254.

https://doi.org/10.1108/BFJ-04-2021-0449

PONTEROTTO, J. G. (2007). an Overview of Coefficient

Alpha and a Reliability Matrix for Estimating

Adequacy of Internal Consistency Coefficients With

Psychological Research Measures. Perceptual and

Motor Skills, 105(7), 997.

https://doi.org/10.2466/pms.105.7.997-1014

Qiang, C. Z., Kuek, S. C., Dymond, A., & Esselaar, S.

(2011). Mobile Applications for Agriculture and Rural

Development. December.

Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0

(beta).

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

194

Sabbagh, M., & Gutierrez, L. (2022). Micro-Irrigation

Technology Adoption in the Bekaa Valley of Lebanon:

A Behavioural Model. Sustainability (Switzerland),

14(13), 1–19. https://doi.org/10.3390/su14137685

Sun, R., Zhang, S., Wang, T., Hu, J., Ruan, J., & Ruan, J.

(2021). Willingness and influencing factors of pig

farmers to adopt internet of things technology in food

traceability. Sustainability (Switzerland), 13(16).

https://doi.org/10.3390/su13168861

Uzhavan Mobile Application. (n.d.).

Factors Inﬂuencing the Use of Uzhavan App by the Farmers

195