Abstract
The development of technology and information in the Industry 4.0 era, especially the internet, is experiencing very rapid progress. Searching for information through new media will increase human resource capacity and work efficiency. Using technology acceptance model (TAM) theory, this study aims to identify social media usage as a communication and information medium and analyze the influence between constructs in social media acceptance. A cross-sectional survey was used in this study design. A structured questionnaire from previous related studies was used as the instrument. One hundred and eleven sheep farmers from several villages in Sukabumi, West Java groups, participated in this survey. Stratified sampling was adopted to select and interview the respondents. The findings show that social media usage as a channel for communication and livestock information is quite adequate. Many farmers already use WhatsApp and YouTube. However, only a few farmers use Facebook and Instagram. The results of the TAM analysis (limited to only the WhatsApp application) indicate that perceived ease of use positively influences perceived usefulness (PU). A positive relationship is also shown between PU and behavioral intention to use. The theoretical implication is that this study proves how social media adopts in disseminating messages and seeking information. Regarding managerial implications, the findings suggest that stakeholders in the agricultural industry across the value chain could enhance their services by fostering a broader ecosystem of social media applications.
1 Introduction
The livestock sector is an essential contributor to a nation’s economic development. It plays a significant role in the gross domestic product, creates employment opportunities, increases people’s income, and generates foreign exchange. Moreover, it has a more comprehensive role in providing food for the community, producing raw materials for the industrial and service sectors, saving foreign exchange through exports or import substitution products, providing capital for developing other sectors, and playing a role in providing environmental services.
The livestock sector relies heavily on information to function effectively. In today’s digital age, communication and information processes have become faster, more comprehensive, and easier to access. Social media has emerged as an effective channel for sharing information and facilitating communication because it allows people to disseminate information quickly and widely to a large audience. Therefore, it is more common for people to use social media to stay informed about the livestock industry [1].
According to the data provided by the Directorate General of Livestock Services for 2020–2022, West Java Province has the largest sheep population, with 11.9 million heads in 2020, 10.03 million heads in 2021, and 9.98 million heads in 2022 [2]. As per the 2021 data, the total sheep population was 10.03 million, which shows a decrease (opendata.jabarprov.go.id). However, this also happened in several other provinces, such as Aceh, West Sumatra, Jambi, Yogyakarta, and Banten.
Sukabumi Regency in West Java has the third largest sheep population in the area, after Garut and Cianjur. According to opendata.jabarprov.go.id, in 2019 the population was 469,871, which increased to 470,586 in 2020, and 472,939 in 2021. However, the increase is not significant, so all stakeholders, especially farmers, need to make efforts to increase the productivity and population of sheep.
Livestock farming is one of the agricultural subsectors. The sheep farming business has been developed in Indonesia for a long time, but its maintenance is still traditional, meaning that the business only meets its own needs and is part-time [3]. Raising livestock is considered as a part of farming work. This condition is reflected in the integration carried out by farmers by combining agricultural businesses with livestock raising [4].
Sheep farming in Indonesia still needs to be well developed and is still being developed on a small scale, namely small-scale farming [5]. Livestock farmers mostly cultivate sheep farming in Indonesia in rural areas. The sheep cultivated are generally in small quantities, 3–5 per family, are raised traditionally, and are part of farming, so the income level is small [6]. Sheep have the advantage of quickly adapting to the environment and being more accessible to care for [5].
Farmers can develop their businesses and increase their livestock population by implementing the latest science and technology. Social media provides access to information on various developing sciences and technologies, making it easier to share knowledge. With the internet penetrating even the most remote villages, there is no excuse for farmers not to be familiar with the most efficient livestock technology. As a result, it is essential to analyze social media’s acceptance level as a communication channel for livestock information. Researchers should research the analysis of social media acceptance as a communication and livestock information channel for sheep farmers in Geger Bitung District, Sukabumi Regency.
The number of internet and social media users in Indonesia is increasing yearly. There were 202.6 million internet users in Indonesia in January 2021, an increase of 15.5% from the beginning of the previous year [7]. In February 2022, internet users reached 204.7 million [8]. The number of internet users increased in January 2023, reaching 212.9 million. Internet penetration in Indonesia was 77% in January 2023 [9]. In February 2022, social media users in Indonesia reached 191.4 million, an increase of 21 million (12.6%) from 2021. However, the number of social media users in January 2023 decreased by 12.7% from the previous year [9].
Industrial Revolution 4.0 is an industrial revolution of the twenty-first century with major field changes. It is a combination of technologies that reduces or eliminates the boundaries between the physical, digital, and biological worlds (Imarketology, 2020 in studies of Gandasari et al. [10]). Industry 5.0, also known as the “human-centered industry,” is the next phase in the evolution of manufacturing and production processes [11]. The basis of the industry becomes human-centered technologies [12]. Like the previous industrial revolution, Industry 4.0 and Industry 5.0 can potentially improve people’s quality of life (Imarketology, 2020 in studies of Gandasari et al. [10]).
This revolution includes innovation and massive use of wireless technology and big data. These innovations solve various social challenges such as AI, big data, IoT, robotics, and the rapidly growing sharing economy. The revolution allows every country to develop and increase its internal capabilities in all sectors. Boundaries between countries will narrow as information exchange becomes more massive in the digital era [13]. Digital transformation will create new values and become a pillar of industrial policy in many countries [14].
The development of the internet and digital technology fundamentally impacts all development sectors, including agricultural and rural development. Farmers today can access agricultural resources via internet through their mobile phones, telephone, or computer. This has happened due to the evolution of digital technology which has stimulated the economy with a progressive increase in agricultural productivity due to the application of modern communication technology in agriculture [15]. Dissemination of appropriate agricultural information will increase efforts to improve welfare and achieve adequate food production. Kiplang’at [16] states that disseminating relevant information to farming communities can increase the effectiveness of adopting agricultural inputs and making decisions in marketing. On the other hand, imperfect information and high transaction costs can be the main obstacles in the farming marketing process.
One of the internet and digital technologies is social media. Social media is a new internet-based media used to communicate information [17] and has become one of the most promising channels for disseminating agricultural information [15]. Social media is becoming an essential platform in agriculture for information seeking, sharing information, selling/buying agricultural commodities [18,19], solving problems, market rates, branding of agriculture commodities [19], and improving the ability of entrepreneurs to find new opportunities [20].
Sasongko’s [21] research results state that business people have widely used computer networks and the internet in carrying out negotiations, transactions, and marketing their products to each of their relations. Internet technology can increase sales, information, and other activities supporting business development in sheep farming [5]. Social media has become an inevitable part of our daily lives, and like it or not, it is here to stay [22]. Businesses that are still not using social media are scrambling to get on board the bandwagon now as they are becoming aware of the enormous power and potential of this medium [22]. So it is necessary to identify whether sheep farmers in Geger Bitung District use the internet, in this case social media, in carrying out their sheep farming business activities.
According to statistical data from the Scopus database between 2011 and 2022, research on the technology acceptance model (TAM) is increasing. Nevertheless, there are still very few TAM publications in the agricultural sector. Out of the 4,082 documents, only 72 are related to agriculture. Some of the few TAM publications in the farming industry include studies on online agricultural marketing [23–26], agrarian financing [17], adoption of agricultural technology [27–31], adoption behavior [32], mobile broadband, and using the Internet of Things for farmers [33,34]. Based on the statistical data, there is still a significant gap in the research conducted in the agricultural sector. Kepios analysis reveals that the total global social media user has increased by close to 30% since the start of the pandemic, equating to more than 1 billion new users over the past 3 years, and GWI’s data also show that people are, spending more time on social media than ever [35]. Therefore, researchers are interested in conducting TAM research on the use of social media in agriculture; in this case, the focus will be on animal husbandry. So, the contribution of this research was TAM research on social media used by sheep farmers, which had never been studied in previous research. This research presented novel data based on a survey conducted on West Java shepherds. There were two research objectives, namely: (1) identifying the use of social media as a medium of communication and information for sheep farmers in Geger Bitung District, Sukabumi Regency and (2) analyzing the influence between constructs on the acceptance of TAM-based social media.
2 Literature review
2.1 Theory of TAM
The adoption of technology, encompassing the recognition, integration, and embrace of novel technological advancements, represents a pivotal aspect of contemporary discourse. The initial phase of this process, known as technology acceptance, is characterized by an individual or organization’s attitude toward technology, influenced by many variables. Consistent with the seminal Innovation Diffusion Theory (IDT) introduced and subsequently refined in 1995, the definition of technology adoption is a strategic decision to harness the overall potential of technological innovation, contingent upon the idea perception, behavior, or product as novel and innovative [36].
Technology acceptance and adoption theories are important in informing investigations into online technology. Some prominent theoretical frameworks in this field include the chronological order presented in IDT by Rogers [37]. Based on the Theory of Reasoned Action (TRA), Davis in 1986 proposed the TAM to predict and explain the acceptance of a new technology, which was further refined in 1989 [17].
TAM has progressed into a crucial framework for comprehending predictors of people’s behavior concerning whether they would accept or reject one technology, particularly in the context of newly introduced technology. Supposing a rationality paradigm, TRA, introduced by Fishbein and Ajzen [38], suggests that intention, a primary predictor of behavior, is influenced by both the attitude toward engaging in the behavior and the perceived social influence (subjective norms). Behavioral intention (BITU), in turn, is characterized as the personal likelihood of performing a specified behavior, with attitude representing the emotional assessment or affective response to the behavior and subjective norm referring to people’s perception that is important to the individual about the behavior [39].
To construct a robust model predicting the effective utilization of any technology, Davis modified TRA by emphasizing attitudes rather than BITUs. The motivation of an individual could be explicated by perceived ease of use (PEOU), perceived usefulness (PU), and attitude to use. Consequently, TAM posited that intention to use technology, which will lead to actual behavior, is influenced by PU and PEOU [39]. Individual attitude toward technology significantly determines whether the user will embrace or discard it. This attitude is shaped by two crucial beliefs: PU and PEOU, with PEOU directly impacting PU. PEOU is the perception degree of an individual in how easy using the technology is. In contrast, PU is the individual perception of how using the new technology would cause an improvement [39].
PEOU and PU have been empirically proven as sound predictive factors significantly influencing the acceptance of learning technologies. In essence, TAM stands as a robust and enduring theoretical framework that continues to shape our understanding of technology adoption and acceptance, particularly in the dynamic landscape of educational technology (Figure 1).
Framework of TAM. Source: Davis et al. [39].
2.2 Social media
Social media is a platform where internet users can communicate with each other freely and access information generated by other users. It includes all services that allow people to share and exchange user-generated content. Social media is a popular public relation tool, enabling people to communicate, exchange ideas, and express their thoughts and opinions with others. Research from Taprial and Kanwar [22], Fitriyah and Nurhaeni [40], and Zhao and Zhu [41] supported this statement.
Social media has several distinct characteristics, as outlined by Taprial and Kanwar [22], which include:
Accessibility: social media is effortless, making it accessible to almost anyone. It provides people with an opportunity to communicate with others.
Speed: social media is fast and allows content to be shared and made available immediately after production.
Interactivity: social media provides opportunities for two-way communication between users. This allows people to interact with each other and engage in conversations.
Longevity/volatility: Once a user posts content on social media that content remains forever. Although users can still edit their content, it is essential to be aware that it may be available online indefinitely.
Reach: social media can be accessed from anywhere any time, making it a powerful tool for communication. It provides a platform for people to share and comment on the content without geographical restrictions.
People use social media for communication, such as to build relationships, strengthen relationships to build networking, and strengthen networking [42]. According to Wok and Idid [43], social media is not only used as a media to communicate, but also its usage has been extended to information sharing [43], such as sharing pictures and videos with their friends [44]. Social networking represents a progressive evolution in online engagement, facilitating communication, content sharing, and collaborative contributions among individuals with shared interests in the digital realm. This platform is a valuable tool for cooperative efforts and exchanging of knowledge within an open-access environment [43].
In the context of social media, TAM-related users perceive platforms like Facebook, Twitter, or LinkedIn as applicable for information sharing and learning due to the instant access to a diverse range of content, discussions, and resources. TAM emphasizes PEOU as a critical factor in influencing users’ attitudes [45,46]. Social media platforms usually have user-friendly interfaces and functionalities, making it easy for users to share information, engage in discussions, and collaborate. Individuals are often influenced by their social networks, peers, and communities [22]. The perception that others find social media valuable for information sharing and learning positively affects one’s subjective norms, contributing to adopting these platforms. Therefore, specific communities that use social media may use it to spread information and learning.
2.3 Research framework
TAM is a robust and valid model commonly employed for measuring the acceptance of several technologies. Several studies have employed TAM in examining the use of various technologies, such as e-learning, e-government, m-learning, wireless technology, web-based training, online banking, m-payment, social media, and wearable technologies [47]. Several previous researchers who used TAM were Mayjeksen and Pibriana [48], Saputra and Misfariyan [49], Al-emran and Granic [47], Al-Emran and Teo [50], Al-Maroof et al. [51], and Yen et al. [52].
Mayjeksen and Pibriana [48] carried out research to determine the level of user acceptance of using online shopping applications using TAM. The results of this research are the seven hypotheses proposed; the seven hypotheses are acceptable and have a positive effect.
According to Saputra and Misfariyan [49], one of the factors that currently plays a vital role in the successful implementation and use of information technology is the user factor. The level of user readiness to accept information technology has a significant influence in determining the success or failure of implementing the technology. So Saputra and Misfariyan [49] conducted research with the aim of identifying factors that can influence users in using the information system and finding out the variables that most impact the ease of use and acceptance of the information system. Saputra and Misfariyan’s research results show that PEOU variables affect PU variables. PU variables affect intention to use variables, and intention to use variables affect the actual use.
Al-emran and Granic’s [47] research results on “A Bibliometric Analysis of the Technology Acceptance Model and Its Applications From 2010 to 2020” show that the number of studies on TAM and its applications is on the rise, indicating that applying, modifying, and extending the model is still valid across several applications and domains. The top ten TAM applications are electronic commerce/e-commerce, Internet banking/online banking, social media/social networks, e-learning, e-government, mobile commerce/m-commerce, mobile learning/m-learning, mobile banking, cloud computing, and augmented reality [47].
The study focuses on the acceptance of social media as a communication and information channel by sheep farmers in Sukabumi Regency. This research used the TAM concept to explain user behavior toward new information technology systems (Figure 2).
Framework of TAM research.
The hypothesis of this research is as follows:
H1: Attitude of use (ATUT) has a positive impact on BITU when using social media as a channel for livestock communication and information.
H2: BITU has a positive impact on the actual of using technology (ATU) social media as a livestock communication and information channel.
H3: PEOU has a positive impact on attitudes toward using (ATUT) social media for livestock communication and information.
H4: PEOU has a positive impact on social media’s PU as a livestock communication and information channel.
H5: PU has a positive impact on attitudes toward using (ATUT) social media as a channel for livestock communication and information.
H6: PU has a positive impact on the ATU of social media as a channel for livestock communication and information.
H7: PU has a positive impact on BITU to use social media as a channel for livestock communication and information.
3 Materials and methods
This study surveyed sheep farmers in Indonesia using a structured questionnaire. This study aimed to gather farmers’ opinions on social media usage for seeking information and communicating with others in the livestock business. This study was built on previous research by Mayjeksen and Pibriana [48]. Data were collected between March and September 2023 using a self-administered offline questionnaire. Enumerators assist in the implementation of filling out the questionnaire by farmers. The population in this study were farmers of Geger Bitung District, Sukabumi Regency in West Java Province. The consideration for selecting this location was because it was a potential area for sheep farming.
The sampling criteria were farmers who were members of sheep farming groups in Geger Bitung District. There are 11 farmer groups with an average number of 15 people per group. So, the population of farmers was 165 people. This study obtained a sample size of 116 people using the Yamane formula. The stratified random sampling was used to get samples from 11 farmer groups in the Geger Bitung District. This sampling technique was taken so that all members of the 11 existing groups could be selected as sample members so that from each group a sample of 10–11 members was taken. However, of the 116 respondents, 111 respondents from 10 farmer groups agreed to fill out the questionnaire. An initial study to assess whether the indicators used are reliable and valid was conducted before the actual study.
Respondents were assured that their anonymity would be protected. The authors confirm that all participants gave informed consent, and the questionnaires were anonymized. The survey instrument had two parts: demographics and constructs. A structured questionnaire was created as a survey instrument based on indicators obtained from the research model adopted by Mayjeksen and Pibriana, where 22 indicators were obtained (Table 1). The indicators were measured using a four-point Likert scale. A four-point scale aims to gather firm and clear opinions, reducing neutrality and reducing ambiguity [53].
Variables/constructs (Construct) along with indicator from TAM [48]
| No. | Variable/construct | Indicator |
|---|---|---|
| 1 | PEOU | Convenience for learning/understood |
| Convenience for use | ||
| Convenience for reaching the objective | ||
| Convenience for interacting | ||
| Flexibility | ||
| 2 | PU | Improve the performance |
| Answering information needs | ||
| Increase efficiency | ||
| Simplifies work processes | ||
| Increase effectiveness | ||
| 3 | Attitude toward using technology (ATUT) | Attitude reception to the system |
| Not boring | ||
| Enjoy use | ||
| Feeling | ||
| 4 | BITU | Motivation to keep using |
| The leading choice in use | ||
| Desire to use the system frequently | ||
| Motivate other users | ||
| 5 | ATU | Frequency of use |
| Duration of use | ||
| The real use of technology | ||
| User satisfaction |
Data analysis involves two types of statistical analysis methods: descriptive statistical analysis with IBM SPSS Statistical 26 and determination analysis of factors influencing acceptance of social media with SmartPLS version 3.
Descriptive statistical analysis is used to represent research data. This type of quantitative research involves using statistics, such as sum, count numbers, mean, and proportion/percentage, to determine descriptive statistics of observed variables, including internal factors (age, education, etc.).
On the other hand, analyzing the factors that influence social media acceptance is based on partial least squares structural equation modeling (PLS-SEM) analysis. In this analysis, there are three simultaneous activities: measurement analysis, structural model analysis, and obtaining a suitable prediction model.
4 Results and discussion
4.1 Demographic profile of respondents
Questionnaires were distributed to 116 respondents. However, only 111 respondents agreed to fill out the questionnaire. From the survey results, there were around 100 respondents who had smartphones, but only 98 people used social media as a channel of communication and livestock information for sheep farmers. So, the number of respondents in this study was 98 people. The demographic profile of respondents is shown in Table 2.
Demographic profile of respondents
| Item and category | Number | Percentage (n = 98) |
|---|---|---|
| Gender | ||
| Male | 97 | 98.98 |
| Female | 1 | 1.02 |
| Age | ||
| 20–30 | 6 | 6.12 |
| 31–40 | 21 | 21.43 |
| 41–50 | 31 | 31.63 |
| 51–60 | 23 | 23.46 |
| >60 | 17 | 17.34 |
| Education | ||
| Elementary school | 61 | 62.24 |
| Middle school | 19 | 19.39 |
| High school | 15 | 15.31 |
| Bachelor’s degree or above | 3 | 40.81 |
| Farming duration (years) | ||
| 0–5 | 43 | 41.84 |
| 6–10 | 38 | 32.65 |
| 11–15 | 8 | 7.14 |
| 16–20 | 12 | 18.37 |
| 21–25 | 0 | 0 |
| 26–30 | 2 | 2.04 |
| 31–35 | 1 | 1.02 |
| 36–40 | 7 | 7.14 |
| Location | ||
| Geger Bitung village | 18 | 18.36 |
| Buniwangi village | 10 | 10.20 |
| Caringin village | 22 | 22.44 |
| Cijurey village | 27 | 27.55 |
| Karangjaya village | 21 | 21.42 |
The results show that 99% of the respondents were male, and 1% was female. This is in line with previous studies [18,19], which shows that agricultural activities, especially production, are mostly carried out by men. Regarding the education level, most farmers (62.24%) graduated from elementary school. Zhang [54] concluded that the lower their educational level, the lower their technology adoption capability. The results show that 40% of the respondents were over 50 years old. This implied that 60% of the respondents were below 50 years. According to Kandagor et al. [15] farmers below 50 years old can access agricultural information through social media such as WhatsApp and Facebook, among others, effectively. The results show that length of farming duration, 41.84% of the respondents were below 6 years. This implied that 58.16% of the respondents were over 5 years. Farmers worked by combining agricultural business with livestock rearing. The main agrarian businesses were food crops and horticulture, while animal husbandry was part-time. So even though their experience in the sheep business was still low, this did not mean their farming experience was also low. According to Sugeng [3] the sheep farming business in Indonesia only meets its own needs and is part-time. Raising livestock is considered part of farming work.
Ninety-eight respondents were from five villages. Eighteen persons were from Geger Bitung village, 10 were from Buniwangi village, 22 were from Caringin village, 27 were from Cijurey village, and 21 were from Karangjaya village.
4.2 Social media usage and TAM analysis
Social media applications are used for communication and information sharing, encouraging individual collaboration and interaction. They provide the latest information and help people stay in touch with each other [55]. Farmers typically use four social media platforms: WhatsApp, Facebook, YouTube, and Instagram. Based on analysis, some farmers use multiple social media platforms, while others use only one, primarily WhatsApp. According to the analysis results (Figure 3), WhatsApp is the most widely used platform among farmers (100% or 98 respondents), followed by YouTube (72.44% or 71 respondents), Facebook (42.85% or 42 respondents), and lastly, Instagram (7.14% or 7 respondents).
Chart of social media used by farmers.
This study found that 100% of the sheep farmers used WhatsApp as a communication and information source to enhance their business technology. The result indicates that WhatsApp has become a prominent channel for farmers to search for information and communicate. WhatsApp is the most likely application among farmers [18,19,56,57]. They chose the WhatsApp application because it is easier, cheaper [18], and accessible [58]. Kumar Panda et al. [59] stated that the conversation application media (WhatsApp) greatly influences farmers’ decisions compared to other social media. A group of dairy farmers in Kenya use WhatsApp to network, train, and mentor other dairy farmers [60]. Additionally, different groups of young farmers in Kenya use WhatsApp groups to share experiences, ask questions, and get advice on how to control pests and diseases and sell their agricultural products [61]. Oil palm farmers in Indonesia use WhatsApp groups to discuss palm oil news and to increase knowledge that farmers can apply regarding their farming business [62].
According to Meltwater [35], the most used social media platform by Indonesians is WhatsApp at 92.1%. Remaining unbeatable in Indonesia, META dominates the Monthly Active User app ranking, with WhatsApp being on top of the list, followed by Facebook and Instagram [35]. Further analysis from data.ai shows that WhatsApp has the highest frequency of use among the world’s most-used social platforms, with 82.6% of the platform’s monthly active Android app users opening the app daily [35]. On the other hand, Instagram was the least popular social media platform used by sheep farmers to search for information, with only 5.79% of them using it for communication and information purposes to improve their business technology.
Social media has become the go-to source for people seeking information as it provides quick and comprehensive access. According to DataReportal [63], in January 2023, there were 167.0 million social media users in Indonesia. Advertising sources Meta and Google have published data indicating that Facebook has 119.9 million users, YouTube has 139.0 million users, and Instagram has 89.15 million users as of early 2023. Additionally, GSMA Intelligence data show that Indonesia has 353.8 million mobile connections. This information suggests that Facebook and YouTube are still more popular than Instagram. This finding is consistent with the research showing relatively lower usage of Instagram than the other social media applications.
The study [1] highlights the trend of relying increasingly on social media to access information. In this study, the research questions to respondents were limited to using social media for livestock activities. This study found that farmers used social media to get information about livestock cultivation, animal health, marketing, and building relationships. Figure 4 shows the theme of information that farmers accessed from social media. Farmers used WhatsApp to access information about cultivation (40 respondents), animal health (23 respondents), marketing (90 respondents), and others (11 respondents); Facebook to access information about cultivation (19 respondents), animal health (10 respondents), marketing (28 respondents), and others (9 respondents); YouTube to access information about cultivation (49 respondents), animal health (45 respondents), marketing (19 respondents), and others (16 respondents); and Instagram to access information about cultivation (1 respondent), marketing (7 respondents), and others (1 respondent).
Chart of the theme of information that farmers access from social media.
This research found that farmers accepted social media as an application to search for sheep farming information. Farmers use WhatsApp not only to access information but also to form relationships with others. Farmers used Facebook and YouTube to access all information, but Instagram to access information about marketing, promotion, and livestock benchmarking in other advanced farmer/farmer groups.
The adoption of technology by users is a necessary step before implementation. Researchers and practitioners must understand user acceptance, adoption, and utilization of contemporary technologies in the real world [55]. Currently, there are limited studies on social media application adoption in agricultural activities. Therefore, further investigation is required to uncover acceptance issues of social media platforms and identify key improvement points. To assess the relationship between constructs in social media acceptance based on TAM is the main objective of this study. In this article, the TAM analysis results and discussion were limited to the social media that respondents used the most. The findings showed that farmers mostly used WhatsApp as their communication channel. Although WhatsApp is the most widely used social media, it is not a social media that is widely researched. According to Meltwater [35], YouTube occupies the first position (67.7%) as a social platform used for work research, followed by Facebook (58.1%), Instagram (53.3%), and WhatsApp (47.1%). Ihsaniyati et al. [64] state that WhatsApp is not the most researched social media. So, the contribution to research on using WhatsApp as social media is still open.
This research uses TAM theory with five items, namely, the usefulness of WhatsApp as a social media perceived by farmers as a medium of communication and information technology for sheep farming businesses (PU), attitudes toward using WhatsApp as a media for communication and information technology for sheep farming businesses (ATUT), PEOU of WhatsApp as a communication and information media for sheep farming business technology (PEOU), behavior of breeders’ intentions to use WhatsApp as a communication and information media for sheep farming business technology (BITU), and the frequency and duration of farmers in using WhatsApp as a communication media and sheep farming business technology information (ATU). Seven hypotheses were developed and tested using the PLS-SEM method and assisted with SmartPLS 3.0 software.
The minimum sample size is ten times the maximum number of indicators containing a construct or the most significant number of structural paths leading to a particular construct in the model [65]. The most complex construct in this research model had five indicators, so the minimum sample size was 50 (10 × 5). Statistical power analysis with GPower application is used to ensure that the study had an adequate sample size to detect significant effects [66]. This study used a PLS-SEM model with five predictors assuming a medium effect size (f 2 = 0.15) with a significance level of 0.05 and a power of 0.80. The results of the statistical power analysis showed that a research sample size of approximately 92 was required to achieve a statistical power of 0.80 with a medium effect size at a significance level of 0.05. This means that a sample of 98 respondents was considered adequate to detect a significant effect.
The statistics for outer loadings mean and standard deviation results showed that convergent validity was achieved (Table S1). An indicator is considered valid since the loading value is greater than 0.7. The result also indicates indicators with a loading factor less than 0.7, such as PU1, PU2, PU3, PEOU1, ATUT3, BITU1, BITU3, ATU3, and ATU4 (Table S2). The centrality and variance of the responses were analyzed using mean and standard deviation. The range of mean values was closer to the average value, and standard deviation values indicate less deviation in responses. After excluding the indicators with a loading factor value of less than 0.7, a revised model was obtained through re-estimation. The results of the revised model are displayed in Figure 5.
Research model results.
Composite reliability (CR) and convergent validity results showed that a minimum value of 0.70 was recommended for CR to indicate high reliability (Tables S3 and S4). The CR values of more than 0.7 means that all indicators are reliable. Convergent validity meets the requirement (AVE value). The root value (square root of average variance extracted) had a higher value for each construct than the correlation between other constructs. This indicates good discriminant validity, as defined by Fornell and Lacker.
The result of cross loading, among the indicators, ATU1 and ATU2 had the highest correlation with the ATU construct (0.922 and 0.923, respectively). In contrast, the ATUT1, ATUT2, and ATUT4 indicators had a higher correlation with the parent construct ATUT (0.889, 0.806, and 0.752, respectively). Similarly, BITU2 and BITU4 had a higher correlation with the BITU construct (0.920 and 0.876, respectively), while PEOU2, PEOU3, and PEOU4 had a higher correlation with the parent construct PEOU (0.760, 0.804, and 0.755, respectively). PU4 and PU5 correlated more with the parent construct PU (0.919 and 0.868, respectively). Other indicators were also strongly associated with the constructs they measured, indicating good discriminant validity (Table S5).
PLS has measured collinearity with the variance inflated factor (VIF). According to Hair et al. [65], the maximum tolerable value for VIF is 5. The results indicated that the VIF value was less than 5. Therefore, there are no severe symptoms of collinearity (Tables S6 and S7).
The result shows (Table 3) that PEOU had a positive impact on PU, with the original sample being 0.429, T statistics being 2.551, and P value being 0.012. A large T statistics and a small P value indicate statistical significance. The study also shows that PU had a positive impact on BITU, with the original sample being 0.576, T statistics being 5.712, and P value being 0.000. The results supported the positive effects of PEOU on PU, and PU on BITU.
Path coefficients and hypothesis testing results
| Hypothesis | Original sample | T statistics (|O/STDEV|) | P values | Result |
|---|---|---|---|---|
| H1: ATUT → BITU | −0.102 | 1.098 | 0.275 | Rejected |
| H2: BITU → ATU | −0.242 | 1.772 | 0.080 | Rejected |
| H3: PEOU → ATUT | 0.210 | 1.517 | 0.132 | Rejected |
| H4: PEOU → PU | 0.429 | 2.551 | 0.012 | Accepted |
| H5: PU → ATUT | 0.077 | 0.525 | 0.601 | Rejected |
| H6: PU → ATU | −0.077 | 0.591 | 0.556 | Rejected |
| H7: PU → BITU | 0.576 | 5.712 | 0.000 | Accepted |
Bold indicates the accepted result.
The research findings indicate that accepting WhatsApp as a communication and information medium for sheep farming has yielded positive results. The impact of PEOU on PU was found to be positive, as well as the impact of PU on BITU. Studies from Nabhani et al. [33], Yolanda [67], Armah and Li [68], and Rauniar et al. [69] supported this study result. All these research studies also showed that PEOU had a positive impact on PU, and PU had a positive impact on the group’s intentions in adopting social media to disseminate messages and seek information.
Most respondents perceive that the use of social media applications is determined by the ease of use of the application, and they perceive that this technology is useful or helpful if the technology is part of their business, thus encouraging them to have the intention to use it. The research found that respondents used WhatsApp because it is accessible for relationships and interactions and to look for livestock information. The information concerned were cultivation, disease, livestock health, maintenance, marketing, selling prices, markets, and livestock manure management. The result is in line with the previous studies [19,33]. Based on the findings in this field, most of the respondents who intend to or already use the application prefer to get benefits on access to information such as updated commodity prices and technology as other players in the industry may do it.
According to Rauniar et al. [69], social media users will better appreciate the minimal effort required to learn the features, use the app, and perform social media-related activities, such as uploading and sharing videos or networking with a professional. In social media, users can rate sites based on how easy they are to use and how effective they are at helping them meet their social media-related needs [69]. Rauniar et al. [69] further state that a user who engages in social media-related activities and experiences benefits from them will develop intentions to use those activities in the future.
This study failed to show a positive impact of PEOU on BITU. Regardless of the previous study result, which showed how PEOU is crucial in TAM, there are situations or conditions where PEOU does not strongly impact the intention. In contexts, content specialization may cause individuals to choose another platform that might be more relevant. Users may also prioritize social media platforms that enable them to make customization and adaptation features for content dissemination, even if the platform is challenging. Studies from Nabhani et al. [33] support the result of this study. Perceive ease of use insignificantly influences intention to use.
Despite a firm intention to use a technology, users might experience unpredictable external barriers to shift their intention into actual behavior. External barriers are access and resources. When individuals experience difficulty accessing the platform, such as internet connectivity or platform accessibility, they cannot reflect their intention to use in actual behavior.
PEOU does not have a positive impact on ATUT. Users cannot accurately perceive the ease of use because of lack of awareness or understanding of technology capability. This unalignment of technology capabilities might prevent users from translating that into actual usability. This is in line with Trihandayani and Abdillah’s [70] research findings, which show that PEOU does not have a significant influence on usage attitudes in social media in small and medium enterprises.
The following argument can explain the insignificant influences of PU on ATUT. Social norms, such as external influences or peer opinions, might make the importance of PU not have any impact on attitude to use. Individuals will put more weight on social considerations than the usefulness of the technology. In contrast to the findings of Trihandayani and Abdillah [70], research shows that PU significantly influences attitudes toward using social media in small and medium enterprises.
The insignificant influences of PU on ATU can be explained with the following argument. The availability of similar platforms might change the perception of users that there is no prominent differentiation among platforms. Therefore, they do not choose one but use them alternately. External barriers such as internet connection might be why PU does not reflect ATU.
The infrastructure needed to use social media is an internet connection. Some problems faced in terms of internet connectivity are no internet connection due to location, poor connection quality, limited quota, and cost-expensive quota fees [71–73]. According to Wiyanto and Prasetyawan [74], more ICT reliability and skills are needed. According to Balkrishna and Deshmukh [19], the problems with using social media in agricultural marketing, such as the adoption of social media as a tool of agrarian marketing, there is limited access to social media because of data and networks, and no training and education about the use of social media in agricultural marketing. This is in line with previous studies [75–77], and lack of hardware or software infrastructure and inadequate technological capabilities are also the problems faced.
Most challenges related to farmers and their responses to social media needs include uninterruptible power lines, intermittent signal problems, quota, and technological capabilities. Some of the social dimensions of innovation and diffusion are needs, technology interests, and sources of support [78]. Modern communication technology in agriculture will increase agricultural productivity [15].
This study’s essential contribution is to comprehensively explore the adoption of social media applications and their implications. The results of our structural model re-establish the significant influences between the original TAM constructs, namely PEOU and PU (H4) and PU and BITU (H7). Collectively, these two hypotheses reflect that users form social media usage intentions because of their usefulness and ease of use.
The results’ findings recommended that stakeholders in the agricultural industry, such as the government, private sector, and community, should create an ecosystem of social media applications to provide better services. The government can provide rural communities adequate infrastructure, internet networks, and digital literacy training. In addition, the government can allocate funds for developing digital applications and platforms that suit the needs of agricultural and rural communities. Private companies can provide innovative digital services and applications. Social media designers and developers have to create value for social media users that help users achieve goals and objectives in using social media. Communities can spearhead promoting and adopting online or digital applications in agricultural and rural areas. The collaboration among the government, the private sector, and the community will accelerate the development of online services, and the community in farming and rural areas can feel the benefits.
5 Conclusion
This research investigated the use of WhatsApp as social media in the livestock sector, using technology acceptance theory as a theoretical framework. The findings in this research were that the PEOU of technology use significantly influenced the PU of technology, and the perception of technology use had a significant influence on the BITU to use technology. Farmers accepted the use of WhatsApp because of its usefulness and ease of use. This shows that PEOU and usefulness were essential factors in social media use. These results will help in understanding the barriers that hinder technology acceptance. Policymakers and related stakeholders need to pay attention to this.
The limitations of this research were the number of samples and geographical location. A larger sample size with a broader range of case study variables from different geographic locations is needed to obtain more accurate results. Second, this research did not examine factors like business environment, individuals, or social influences. According to Nabhani et al. [33], Nurlaela et al. [18], and Pentina et al. [79], business and individual factors as well as social influences are related to technology acceptance. Therefore, further research is needed that reveals the impact of environmental factors on business, personal, and social influences. Finally, this research focused mainly on the acceptance of WhatsApp by farmers. Still, it did not consider the use of this technology based on age and did not consider a comparative analysis with other social media, so further research is needed to consider a comparative analysis of the use of this social media based on age and by comparing it with other social media. According to Meltwater [35], social media channels used for business-to-business research also vary by age. However, GWI’s latest employment survey shows that YouTube tops the rankings across all age groups. In comparison, Instagram is more widely used among the younger age group, while the older generation prefers Facebook and WhatsApp. Therefore, a comparative analysis of social media use based on platform and user age or on different agricultural commodities can provide richer insights into the role of social media in agricultural communications.
Acknowledgements
The authors are grateful for the support provided by Polbangtan Bogor, Binus University, and BPP Geger Bitung for this research.
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Funding information: This manuscript is partially funded by Bina Nusantara University and Multimedia Nusantara University.
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Author contributions: All authors have accepted the responsibility for the entire content of this manuscript and consented to its submission to the journal; reviewed all the results and approved the final version of the manuscript. DG is responsible for conceptualization, methodology, formal analysis, writing – original draft, writing – review, and editing. DD is responsible for the investigation, writing – the original draft, writing – the review, and editing. DT is responsible for methodology, formal analysis, and investigation. OA is responsible for formal analysis, investigation, and visualization.
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Conflict of interest: Authors state no conflict of interest.
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Informed consent: The authors confirm that all participants gave informed consent, and the questionnaires were anonymized.
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Ethical approval: As this manuscript does not involve research on humans or animals, nor does it include vulnerable populations, an ethical statement is not applicable.
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Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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