How to cite:
Anton Susanto, Ali Agus, Jangkung Handoyo Mulyo, Hakimul Ikhwan.
(2022). The Impact of Online Community on Farmer Empowerment:
A Strategical Analysis for Development the Technology-to-
Performance. Journal Eduvest. Vol 2(6): 1.164-1.175
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Eduvest – Journal of Universal Studies
Volume 2 Number 6, June, 2022
p- ISSN 2775-3735- e-ISSN 2775-3727
THE IMPACT OF ONLINE COMMUNITY ON FARMER
EMPOWERMENT: A STRATEGICAL ANALYSIS FOR
DEVELOPMENT THE TECHNOLOGY-TO-PERFORMANCE
Anton Susanto
1
, Ali Agus
2
, Jangkung Handoyo Mulyo
3
, Hakimul Ikhwan
4
School of Graduate Studies, Gadjah Mada University, Indonesia
Email: [email protected]om, al[email protected]c.id, JHandoyoM@gmail.com,
hakimulikhwa[email protected]
ARTICLE INFO ABSTRACT
Received:
May, 26
th
2022
Revised:
June, 13
th
2022
Approved:
June, 17
th
2022
This research provides empirical evidence for the benefit of
social media in the online farmer community. The benefit that
focus on empowerment value. The case study is used to explore
the ICT usage in the community of Laying Hens Farmer in East
and Central Java implementing the multi-feed and additive
supplement. The research develops The Technology-to-
Performance Chain (TPC) approach to analysis the strategy of
farmer empowerment. The performance that was addressed
by empowerment values. The study results that the farmer’s
empowerment are influenced significantly by task-technology
fit with a path coefficient of 0,704. It means that utilization of
social media as the online media of community will encourage
empowerment if the information and knowledge sharing
activities inside are related to the task or farmer's livelihood.
The farmer's characteristics and the facilitating condition
affect the empowerment insignificantly. However, the
facilitating condition determines the task-technology fit with
the path value 0,868. This study discusses 3 important layers
in farmer empowerment strategies through online
communities. The first is the underlying factors which includes
internet access, product innovation and agents of change
(facilitators/agricultural extensionists), the second is the
adoption process (both internet adoption and product
knowledge adoption) while the third is the impact of
empowerment which is direct (output) and indirect (outcome).
Anton Susanto, Ali Agus, Jangkung Handoyo Mulyo, Hakimul Ikhwan
The Impact of Online Community on Farmer Empowerment: A Strategical Analysis for
Development the Technology-to-Performance 1.165
KEYWORDS
Online Community, Empowerment, Technology-To-
Performance Chain
This work is licensed under a Creative Commons
Attribution-ShareAlike 4.0 International
INTRODUCTION
The issue of community empowerment has become a concern after many challenges
emerged from the development of communication technology and the digital economy. The
utilization of technology is highly dependent and constrained by socio-economic
conditions (Authorities et al., n.d.; Torero & von Braun, 2005; Ullah, 2017). Digital
inclusion inadvertently reproduces inequality and exploitation. The emergence of
individual entrepreneurs tends to relocate the locus of development responsibility to the
poor themselves (Yu, 2017) and even strengthens existing power. The distribution of power
is also an issue in the development of smart farming (Regan, 2019; van der Burg et al.,
2019). Market structure and trade are also their obstacles in the development of the digital
economy (Kumar, 2014).
In Indonesia, the development of the digital economy in the agricultural or rural
sector faces more complex challenges. Many factors influence the adoption and utilization
of the digital economy. These factors are the condition of the digital divide in farmer
households (Susanto, 2018), demographic, socio-economic conditions, market system, and
trade structure. However, several studies describe that digital technology is used to
encourage farmer empowerment. Empowerment is observed by increasing access to
information ((Narayan-Parker, 2002), (Kumar, 2014); (Babu & Asokhan, 2010; Khushk et
al., 2016), increasing decision-making abilities (Jairath & Yadav, 2012; Khushk et al., 2016)
supply chain efficiency, rural business development (Galloway et al., 2011; Kour et al.,
2019). Therefore, the use of digital technology by farmers in Indonesia is an important
thing to be observed.
This study takes a case study of developing an online community for laying hens in
Central and East Java. A community formed due to the development of multi-feed and
additive supplements to encourage egg productivity and economic sustainability for
farmers. The research develops the technology to performance chain framework to
strategical analysis in empowering the farmers. Previous researches have used a behavioral
information approach, but they were limited to the adoption of a technology (technology
acceptance model) and have not yet reached the benefits of technology adoption (Shah et
al., 2013; Zaremohzzabieh et al., 2015). The Technology to Performance Chain (TPC)
approach that has been introduced by Goodhue and Thompson in 1995. TPC was used to
see the performance impact of using online communities for the farmer empowerment. The
empowerment of farmers is the actualization of the performance of an information
system/digital technology used. Therefore, this study tries to answer how the online
community affects the farmer empowerment and what strategies to develop the
empowerment.
RESEARCH METHOD
This research is a case study of an online community of laying hens using a multi-
feed and additive supplements. An online community excised along with the on-farm
product innovations development for chicken farms. The online community uses social
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1.166 http://eduvest.greenvest.co.id
media: YouTube Channel and WhatsApp Group. The use of social media began as the part
of digital marketing from the development of multi-feed products and additive
supplements. However, the online communities are formed and have members spread
across the provinces of Indonesia. There are 109 members in the online community which
consist of farmers, extension agents, off-taker agents, and suppliers of input products. Most
of the community members are in East Java and Central Java, so the analysis in this study
uses a majority area approach in observing several conditions or regional factors that
influence them.
Data were collected using online surveys and in-depth interviews with farmers and
business actors in this online community. By with some limitations, the online survey was
only collected by 32 respondents. Data collected were processed using the PLS structural
equation model approach. It was processed by smartPLS software to analyse the structural
relationship between factors in the development of online communities for increasing
farmer empowerment.
Model Development
The Technology to Performance Chain (TPC) approach is the one of approaches in
behavioural information systems. This approach explains that the positive impact of
technology/information systems on the performance of its users will happen if the
technology or information system is utilized and supports the tasks of its users. The Task-
Technology Fit construct affects the utilization and the performance of individual users
directly or indirectly. Figure 1 shows a simple construction of the Technology to
Performance Chain. The use of technology in its development is either mandatory or
voluntary. For a mandatory technology, the task-technology fit construct will influence the
performance impact directly. However, for technologies whose use is voluntary, the
intensity/involvement of users in the use of technology becomes a moderating variable in
the performance impact.
Figure 1 The Technology to Performance Chain Model (Simplified)
In the context of this research, the use of social media in online communities has
been common and formed since 2019. Active users in online communities were conducted
in this survey. Therefore, we ruled out the utilization variable in this study. The task-
technology construct is a concern to be observed. In this study, the performance impact is
proxied as the empowerment impact of farmers. The Empowerment of farmers is reflected
in the ease of access to information (Babu & Asokhan, 2010; Kumar, 2014; Narayan-Parker,
2002; Ullah, 2017) increased knowledge & psychology (Jairath & Yadav, 2012; Khushk et
al., 2016; Rashid et al., 2016) economic & social impacts (Atkinson & McKay, 2007; Khushk
et al., 2016; Lokeswari, 2016; Rashid et al., 2016; Ullah, 2017; Walter et al., 2017).
Meanwhile, task-technology fit is influenced by the farmer characteristics and facilitating
conditions. Figure 2 shows the structural model of research. Then Table 1 shows the detail
of indicators used in building each construct of the research. This study proposes 5 (five)
hypotheses of structural relationships as follows:
Task-Technology
Fit
Utilization
Performance
Impact
Anton Susanto, Ali Agus, Jangkung Handoyo Mulyo, Hakimul Ikhwan
The Impact of Online Community on Farmer Empowerment: A Strategical Analysis for
Development the Technology-to-Performance 1.167
H1: There is a relationship between Task-Technology Fit and Empowerment Impact.
H2: There is a relationship between Farmer Characteristics and Empowerment Impact.
H3: There is a relationship between Facilitating Condition and Empowerment Impact.
H4: There is a relationship between Farmer Characteristics and Task-Technology Fit.
H5: there is a relationship between Facilitating Condition and Task-Technology Fit.
There are eight reflective indicators to measure empowerment impact. These
reflective indicators are the increased access to daily egg price (SE1) as a benchmark for
farmers to determine the selling price, the ease of access to the tools and equipment market
(SE2), and the community strengthening (SE3). Others, the increased chicken population
(SE4), higher selling price (SE5); innovation capability (SE6), innovation adaptability
(SE7), and research ability (SE8). Likewise, the characteristics of farmers are reflected in
the farmer's age, education, land area, the number of chicken populations, variations in
income/work, and experience in raising livestock. Task – Technology Fit consists of
indicators of ease of use/interaction in online communities (TT1), ease of learning of
content (TT2), ease of problem-solving in the field (TT3), content supporting livestock
management (TT4), the accuracy of information (TT5) and clarity information (TT6).
While the Facilitating Conditions consist of mentoring & consultation (EP1), availability
of internet access (EP2), expert opinion (EP3), practice comparison (EP4), and product
enhancement and innovation (EP5).
Fig. 2 The structural model of the research
Table I Latent Variables And Indicators
No
Construct
Code
Indicator
1
Empowerment
impact
SE1
access to daily egg price
SE2
access to tools & equipment market
SE3
community strengthening
SE4
increase in chicken population
SE5
higher selling price
SE6
innovation capabilities
SE7
innovation adaptability
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SE8
research ability (trial error)
2
Farmer
Characteristics
age
education
land area
chicken population
variety of income sources
farming experience
3
Task-Technology Fit
TT1
ease of use
TT2
ease of learning
TT3
ease of problems solving
TT4
content appropriateness
TT5
information accuracy
TT6
information clarity
4
Facilitating
Condition
EP1
assistance and consultancy
EP2
internet access availability
EP3
support/expert opinion
EP4
practical comparison
EP5
Product enhancement & innovation
In this study, several indicators are invalid and not significant in reflecting the
construct. Therefore, these indicators are removed from the model. Then re-estimation of
the structural model is carried out. And the results can be seen in Figure 4. The re-
estimation result shows the loading value of the indicators on the latent construct which
valid model. Table II shows the results of the outer loading of the re-estimated model. R-
Square values for endogenous variables, Task-Technology Fit and Empowerment Impact
are 0.801 and 0.504 respectively. It means that the Task-Technology Fit can be explained
together with the Farmer Characteristics and Facilitating Condition variables of 80.01%.
Meanwhile, the Empowerment Impact is explained by Task-Technology Fit, Farmer
Characteristics, and Facilitating Condition for 50.4%.
Anton Susanto, Ali Agus, Jangkung Handoyo Mulyo, Hakimul Ikhwan
The Impact of Online Community on Farmer Empowerment: A Strategical Analysis for
Development the Technology-to-Performance 1.169
Fig. 3 Initial estimation of the model.
Fig. 4 Re-Estimated Result Model
Table 2 Outer Loading Of The Model
Indicators <- Latent Construct
Loading
Values
Tstat
PValues
Age<-Farmer Characteristic
0,657
2,267
0,0234
ChicekenPop <- Farmer Characteristic
0,732
4,510
0,0000
EP1 <- Facilitating Condition
0,907
24,607
0,0000
EP2 <- Facilitating Condition
0,678
3,707
0,0002
EP5 <- Facilitating Condition
0,863
11,373
0,0000
Experience <- Farmer Characteristic
0,811
4,191
0,0000
LandArea <- Farmer Characteristic
0,842
3,741
0,0002
SE1 <- Empowerment Impact
0,914
5,609
0,0000
SE2 <- Empowerment Impact
0,777
3,571
0,0004
SE3 <- Empowerment Impact
0,778
3,403
0,0007
TT1 <- Task-Technology Fit
0,669
3,554
0,0004
TT2 <- Task-Technology Fit
0,619
3,119
0,0018
TT3 <- Task-Technology Fit
0,838
18,614
0,0000
TT4 <- Task-Technology Fit
0,761
7,806
0,0000
TT5 <- Task-Technology Fit
0,675
4,105
0,0000
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Overall, the model shows good reliability where the composite reliability value is
above 0.7. Likewise, the convergent validity is sufficient with the Average Variance
Extracted (AVE) value above 0.50. Its means that each indicator validly reflects the latent
construct/variable.
The results of the inner weight from the model are shown in Table IV. A significant
relationship between variables/constructs occurred between Task-Technology Fit and
Empowerment Impact with a path coefficient value of 0.704. Likewise, the relationship
between Facilitating Condition and Task-Technology Fit shows a significant value with a
path coefficient of 0.868. Structural relationships between other variables also occur, but
the value is insignificant. There is even a negative coefficient value. It is explained further
in the explanation of each research hypothesis.
Table 3 Realibility And Construct Validity
No
Construct
Composite
Realibility
Average Variance
Extracted (AVE)
1
Empowerment impact
0,865
0,682
2
Farmer Characteristic
0,848
0,583
3
Task-Technology Fit
0,839
0,514
4
Facilitating Condition
0,861
0,676
Table 4 Inner Weight Result
No
Constructs
Path
Coeffficient
T-Stat
P-Value
1
Task-Technology Fit ->
Empowerment Impact
0,704
2,049
0,040*
2
Farmer Characteristic ->
Empowerment Impact
0,027
0,159
0,873
3
Facilitating Condition ->
Empowerment Impact
0,017
0,045
0,946
4
Farmer Characteristic -> Task-
Technology Fit
-0,073
0,660
0,509
5
Facilitating Condition -> Task-
Technology Fit
0,868
13,405
0,000*
RESULT AND DISCUSSION
Based on the results of the structural model analysis, this study finds the answer to
the research hypothesis as below:
H1: The relationship between Task-Technology Fit and the Empowerment Impact
The path coefficient shows a value of 0,704 and a t-statistic of 2,049. The relationship
between Task-Technology Fit and the Impact of Empowerment is positive and significant
at α 5%. It means that Job-Technology Fit affects Farmer Empowerment. The
Anton Susanto, Ali Agus, Jangkung Handoyo Mulyo, Hakimul Ikhwan
The Impact of Online Community on Farmer Empowerment: A Strategical Analysis for
Development the Technology-to-Performance 1.171
implementation of technology or information systems will impact the empowerment of
farmer communities if the technology/information system is following their
work/livelihood needs. This suitability was the ease of use of technology, ease to learn
technology, appropriateness of content in solving problems, content according to farming
management, and accurate information.
H2: The Relationship between Farmer Characteristic and Empowerment Impact.
The path coefficient value is at 0,027 and not a significant t-statistic value. This study finds
the influence of Farmer Characteristics on Farmer Empowerment, but this is not significant.
H3: The Relationship of Facilitating Condition and Empowerment Impact.
The Facilitating Condition has a positive relationship with the Empowerment Impact with
a path coefficient value of 0,017. However, the value is insignificant because it only has a
t-statistic value of 0,045.
H4: The Relationship between Farmer Characteristics and Task-Technology Fit.
The path coefficient test results show a negative relationship between Farmer
Characteristics and Task-Technology Fit. These are explained in these conditions. The
influence of age in adopting technology, the older tend to low familiar than the younger in
technology. The online media is used to share knowledge or experience. It will tend to be
considered normal or not very interesting for the farmers who have many experiences of
raising livestock/farming. They tend to be less enthusiastic in discussing and sharing
knowledge. Inversely, the new ones are enthusiastic because they are still learning and
trying to develop their farming skills. As for the condition of the livestock population, it
can affect the opportunity for the farmers to be active in online communities because of
their busy life. In some cases, in the field for small to medium-scale on-farm farms,
maintenance management is still highly dependent on self-activities and not to have an
employee for cost-efficiency reasons.
H5: The Relationship of Facilitating Conditions and Task-Technology Fit.
This study shows that Facilitating Condition has a positive and high relationship with Task-
Technology Fit. The path coefficient value is 0.868 with a significant t-statistic even at the
1% level. We conclude that the indicators of the Facilitating Condition variable, namely
Mentoring & Consulting, Internet access, and Products enhancement/innovation support
the use of online media in community development. The social media used in the farmer
social learning need conducive facilitation conditions. There is a role of
mentoring/extension agents in coloring discussion and interaction of problems through
online community media. The internet access drives importantly the smooth activities in
online communities. Meanwhile, the development/innovation of multi-feed products and
additive supplements are strategic solutions in on-farm management
Strategical Analysis of Farmers Empowerment
Various factors influence the impact of farmer empowerment through the
development of online communities. Based on the TPC quantitative model, it was found
that's very important to pay attention to 3 (three) layers in the strategy of developing farmer
empowerment through online communities. The first is the underlying factors which
include internet access, product innovation and agents of change (facilitators/agricultural
extensionists), the second is the adoption process (both internet adoption and product
knowledge adoption) while the third is the impact of empowerment which is direct (output)
and indirect. direct (outcome). Figure 5 shows the transformation from TPC model to
strategic layer.
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Figure 5. Strategical Layer Of Farmer Empowerment
Underlying Factors
The underlying factors are the factors that form the basis for the formation of an
online farmer community. The first factor is the development and innovation of multi-feed
products and additive supplements (product innovation). This study found some notes in
the innovation of the product. First, product innovation should increase the added value of
products produced by the farmers. Interest and tangible evidence of the use of this
innovation felt by farmers will encourage the others. The online community is a form of
value co-creation that involves in product development. It is reflected by farmer practices
shared in online community and any farmer’s experiment. The product innovation should
become the framework of sustainable development. Multi-nutrient products and additive
supplements are agricultural innovations in chicken farming. This innovation has relied on
probiotics and multi-nutrient utilization. It will be an advantage in growth and livestock
production. Therefore, it will maintain the sustainability of the livestock business. The
innovations encourage eco-friendly farms, strengthen production stability and productive
period.
The second factor is assistance and consultancy. This role was played by extension
workers as the agents of change at once as agent of product sales. They work to facilitate
the product deployment to farmer, give assistance and build interaction among farmer and
other actor in the community. The third factor is internet access. Although this study uses
farmers in Central Java and East Java, there are still limitations in internet access by the
farmers. Online activities are not possible without equitable internet access with sufficient
bandwidth speed. The development of ICT infrastructure is important for equitable access
in areas that are still a blank spot.
By paying attention to the important role of these underlying factors, institutional
strengthening becomes the main issue in the strategy of developing farmer empowerment
through online communities. This institutional development can be directed at networked
socio-entrepreneurs. This is because community development is based on the values of
farmer empowerment. Even though there is a profit motivation, the values of empowerment
make the relationship between actors complementary as also discussed by (Murphy &
Coombes, 2009). Networked socio-entrepreneurs will also be able to encourage the
situational learning process of farmers (Hasdiansyah & Suryono, 2021) and become a forum
for co-creation for the development of farming practices.
Adoption Process
The limited human resources and the characteristics of laying hen’s farmers have
affected their involvement and interaction in the online community. Demographic factors
affect the level of farmer adoption, both digital adoption (interaction in online
Anton Susanto, Ali Agus, Jangkung Handoyo Mulyo, Hakimul Ikhwan
The Impact of Online Community on Farmer Empowerment: A Strategical Analysis for
Development the Technology-to-Performance 1.173
communities) and product knowledge adoption. Older farmers tend to be more difficult to
adopt in online communities, therefore the role of extension agents is to accompany
farmers' practices as well as share their experiences in online communities. The study found
that there is a paradox of experience for farmers. The experience of other farmers in
adopting product knowledge is not necessarily accepted by experienced farmer.
Psychologically they are confident enough to practice product knowledge according to their
respective experiences. This condition is indeed important as input in the development of
product practice, however, if it is not accompanied by direct consultation and assistance,
the results may differ from expectations.
The other condition that limits farmers' adoption of online communities is time
constraints. The scope of agricultural control (land area and production capacity) become
the boundaries of online adoption. Small-scale farmers still base all activities on their own
or with their families. Therefore, they become passive members of the online community.
With these conditions, strategic steps are needed. The development of a cooking book
needs to be done as a data bank from various experiences and practices of farmers. This
cooking book can serve as a guide to farming practices, but still requires active assistance
or consultation from extension agents. The form of interaction can be done online or offline.
Then in general, with the existence of the cooking book, digital literacy training programs
for agricultural SMEs can be carried out in a combination of digital knowledge and product
knowledge.
Empowerment Impact
This study finds that the empowerment impact of the farmer is visible. It is still
limited to increase the access of market information, both the daily price of egg products
and input products such as livestock equipment and supplies. Likewise, the strengthening
of networks in the community is an empowerment impact. These are the direct impact
(output) of the empowerment impact. However, the impact of empowerment in the form of
outcomes for farmers is still felt to be limited. Whereas the online communities are used as
a means of sharing experience and learning media. It has encouraged the proper use of
multi-feed products and additive supplements. It also can improve the quality of production
and egg products produced by the farmers. However, there are still few farmers who can
add value to these products to increase their empowerment.
Several factors make the output and outcome unable to occur at the same time. These
were the input market (animal feed and DOC/Daily Old Chicken), and the trading structure
of egg products, especially in Central Java and East Java. The same factors are confirmed
in the earlier research. It is about the existing condition of the socio-economic and market
structure influenced the development of the digital economy in the farming and rural sector.
Although it has not changed the existing market structure, the online community can
encourage the market development to a specific market niche. In the context of high-quality
egg products, it can capture the upper-middle market segment. Online communities have
utilization in expanding off-taker networks and developing farmers' entrepreneurial skills.
CONCLUSION
This study has provided an overview of the need for a strategy for developing
farmer empowerment through an online community with 3 layers, namely the underlying
factor, the adoption process, and the impact of empowerment. The three layers are
obtained from the development of the Technology to performance chain model.
In the context of underlying factor, this research confirm that these factors are the
foundation of online community development. Without these factors, online communities
will not be formed and run well. Internet access is an absolute requirement for online
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interactions. Agricultural extension agents are key actors who encourage information
dissemination on the benefits of share-practice and solutions to farmers' problems.
Meanwhile, product enhancement & innovation is the root of the real benefits of
sustainable agricultural innovation. These three factors can involve many actors such as
the government, universities, and the private sector. Therefore, the triple-helix model is
a form of cooperation of ICT in agriculture. In practice, business implementation can take
the form of a socio-entrepreneur or a mutual-benefit business.
In the context of adoption process layer, the use of social media in the development
of online communities can impact the empowerment if their use is following the needs of
farmers in terms of increasing knowledge on on-farm skills, knowledge of products that
can increase added value, and providing solutions to farmers' problems. Therefore, it is
what makes task technology fit as an urgent factor in digital technology in agricultural
communities. This research recommends concern of digital knowledge and product
knowledge when developing the ICT in agriculture. Product knowledge is formed by the
evidence of the use of an agricultural product/innovation in the field, how to use the
product/innovation, and the participation of farmers as part of developing agricultural
products/innovations.
Then, in terms of the impact of empowerment. This study concludes that strategic
efforts are still needed to reduce the gap between output and outcome. The existence of
an online community has helped access information and production facilities, however,
the added value of the product (wider market access) has not been felt by all farmers.
Many limiting conditions are affected by market structure and t(Authorities et al., n.d.;
Jairath & Yadav, 2012; Khushk et al., 2016)rading. Online community as a solution if the
development of the network is directed at strengthening the market niche for the middle-
up class of quality egg products.
Because the size effect is small, a future study needs to identify additional factors or
variables that affect Task-Technology Fit. In addition, the following research needs to
develop the networked socio-entrepreneur as one of empowerment model that combine
between business, social and networking for the context of agriculture development.
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