Information technology adoption in Indonesia’s small-scale dairy farms

Sofiyanti Indriasari; Dana Indra Sensuse; Yuni Resti

doi:10.1515/opag-2022-0304

Article Open Access

Information technology adoption in Indonesia’s small-scale dairy farms

Sofiyanti Indriasari , Dana Indra Sensuse and Yuni Resti

Published/Copyright: June 7, 2024

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From the journal Open Agriculture Volume 9 Issue 1

Abstract

The use of modern digital technologies contributes to small-scale dairy farmers enhancing their business processes, increasing productivity, and addressing challenges related to sustainability. Small-scale dairy farmers are the ones most left behind in the process of implementing information technology. Therefore, there is a need to implement information technology that pays attention to the beneficial aspects seen from the aspect of small-scale farmers. The level of information technology application and human resource readiness can be measured as a basis for formulating strategies for implementing information technology. This research aims to assess the factors that affect information technology adoption among small-scale dairy farmers in Indonesia. The factors that influence the level of adoption of new information technology for small-scale dairy farmers were identified by literature studies, surveys, data analysis carried out using the Partial Least Squares-Structural Equation Modelling approach, and patterns related to aspects of technology application for small-scale dairy farmers. The findings in this research show that the factors that influence information technology adoption for small-scale dairy farmers consist of Digital Skill, Supporting, Perceived Ease of Use, Perceived Usefulness, Attitude Toward Use, and Behavioral Intention to Use. This research identified the pattern that the more complete the recording habits, the higher the level of farmer adoption of the use of new information technology. Apart from that, farmers with moderate incomes show a higher readiness to embrace technology, possibly driven by their aspiration to advance their businesses. Contrastingly, higher-income farmers exhibit less enthusiasm, potentially due to contentment with their current status and apprehension towards new risks. By offering a nuanced analysis of these factors, this study contributes significantly to the literature on agricultural information technology adoption, particularly within the context of small-scale dairy farming. It presents a novel understanding of the barriers and enablers to technology adoption, advocating for more personalized and contextually relevant support strategies. Furthermore, this research provides valuable insights for policymakers, extension agents, and technology developers on designing and implementing information technology adoption strategies that are not only technically viable but also socially and culturally acceptable.

Keywords: dairy farm business process; dairy farm technologies; farmers adoption of information technologies; farm management information system; information technology strategy; small-scale dairy farming

1 Introduction

The digitalization of dairy farming is developing quite rapidly. The digitalization process is starting to be widely carried out in the dairy farming sector. Information technology has an important role in improving dairy farming business processes. However, there is a gap in the process of adopting new technology between small-medium companies and large players in the agricultural sector [1]. Efforts to promote agricultural technology in developing countries must be adapted to local agricultural and cultural contexts [2]. Moreover, understanding of digital agriculture in developing countries is at an early stage [3]. Therefore, the new technology implemented has quite big challenges related to farmers’ knowledge of the use of technology.

Improving small-scale dairy farming business processes can be achieved through the implementation of information technology [4, 5, 6, 7]. The lack of usage of contemporary information technology solutions by small-scale farms is a major hurdle, mainly due to high costs, complexity, and the level of computer literacy needed [8]. However, the introduction of simple and easy-to-use computerized data recording and analysis systems can provide a new approach to record-keeping methods among small and medium-scale dairy farmers. Integrating and homogenizing data from different sources through a centralized hub can enrich analyses and improve farm management decisions. The use of modern digital technologies and mechanization means can also contribute to the efficiency and competitiveness of small-scale dairy enterprises. By leveraging information technology, small-scale dairy farmers can enhance their business processes, increase productivity, and address challenges related to sustainability and competitiveness in the industry.

The digitalization process for dairy farmers is not only about technology but also about human adoption. The introduction of modern digital technologies in dairy farming aims to improve efficiency, sustainability, and animal health monitoring [9]. However, the low rate of digitalization in dairy farming can be attributed to a lack of understanding among top executives on how to transform their organizations using information technologies [10]. To address this, the creation of a perspective enterprise architecture model that incorporates modern digital technologies is suggested [11]. The use of digital technologies, such as IoT, intelligent sensors, and AI, has shown significant improvements in the automation of production, farm management systems, and data processing [11]. Adoption rates of digital technologies vary among different agricultural enterprises, with dairy farming being the most common [12]. The adoption of digital technologies is influenced by factors such as farm characteristics. Generally, small-scale dairy farmers are the ones most left behind by the digitalization process. Digitalization is at the level of cooperatives or middlemen who cover it. Digitization is also carried out by government institutions as well as researchers to obtain data and build databases for new systems. However, many of these digitization activities cannot be accessed by smallholder farmers. Therefore, there is a need for digitization that pays attention to the beneficial aspects seen from the aspect of smallholder farmers.

Several studies have used the Technology Acceptance Model (TAM) and Partial Least Squares-Structural Equation Modelling (PLS-SEM) approaches to study technology adoption models in various agricultural contexts. This is important for identifying technology adoption among farmers [13,14]. The TAM helps in understanding and analysing users’ needs and technology requirements, which is crucial for the success of interactive systems and the design of information systems [15]. It aids in forecasting understanding, technology acceptance, and improving the quality of work, reducing training and support costs, and enhancing customer or user satisfaction [16]. The PLS-SEM has been used in several studies to examine the adoption of information and communication technologies (ICTs) in the agricultural sector. The studies have explored the factors influencing ICT adoption in different contexts, such as wholesale electronic trading in agriculture in India [17], ICT adoption and utilization by smallholder farmers in Indonesia [16], and the use of PLS-SEM in information systems research design [18]. PLS-SEM has proven to be a valuable tool for understanding and modelling the adoption of ICTs in the agricultural sector, particularly in small populations [18].

Small-scale dairy farmers face several problems when it comes to digitalization. One of the main issues is the lack of clear understanding among top executives on how to transform their organizations using modern information technologies [4]. Additionally, small-scale farmers often have inadequate access to resources necessary for practicing dairy farming, which poses a challenge to digitalization efforts [19]. These challenges highlight the need for small-scale farmers to receive adequate knowledge on environmental conservation and proper dairy farming methods to maintain sustainable livelihoods [20]. Overall, the adoption of digital technologies in small-scale dairy farming can help improve productivity, fresh milk quality, and farmers’ income, but it requires addressing the barriers and challenges specific to this sector [21].

This research aims to assess the factors that affect information technology adoption among small-scale dairy farmers in Indonesia. Looking at information technology adoption prior to implementation provides organizations with important insights into the potential benefits, risks, and level of preparedness. This data-driven approach leads to more informed decisions, increased chances of successful implementation, and better results. This research offers a significant contribution by developing a model to assess information technology (IT) adoption levels among small-scale dairy farmers, utilizing (PLS-SEM). It sheds light on the factors that influence technology adoption and readiness, providing insights for enhancing IT implementation in dairy farming business processes. This study enriches the academic discourse on agricultural technology adoption and presents a practical approach to improve efficiency and productivity in the small-scale dairy sector.

2 Materials and methods

Digitalization does not only pay attention to the technological aspects implemented. Another aspect that must be considered for successful digitalization is human resources. How ready they are to make changes due to the implementation of new technology? What strategies are carried out so that the implementation of this technology can be successful? Therefore, the level of technology adoption and the readiness of human resources can be measured as a basis for developing strategies for digitalization. The main activity in this research is to build a model that can later be used to measure the level of adoption of technology for small-scale dairy farmers in the use of information technology. The adoption of modern technology in agriculture can be very important for increasing the productivity and welfare of poor farmers in developing countries [22].

2.1 Research methodology

The research stages for IT adoption model identification for small-scale dairy farming can be seen in Figure 1. This research identifies factors through literature studies and interviews with experts. The results of identifying success factors for the adoption of information technology on small-scale dairy farms are used to create a hypothesis model that must be tested through surveys and quantitative analysis. Data were collected through a survey by distributing questionnaires with research instruments that had been designed based on hypotheses. Quantitative analysis was carried out using the (PLS-SEM) and statistic descriptive technique. SEM is a set of statistical techniques used to measure and analyse the relationship between unobserved variables (latent variables) and manifest variables or observed variables (indicators) [23]. Data preprocessing is rechecking the data collected from the survey results. If there are any blanks, they are immediately confirmed by the respondent. Thus, the data do not have missing values. Data from the questionnaire survey results are ready to be analysed. Data analysis was performed using PLS-SEM modelling.

Figure 1

Research stages.

There are five steps in SEM: model specification, identification, parameter estimation, model evaluation, and model modification [24]. The model evaluated looks at its reliability value. The acceptable reliability value based on Cronbach’s Alpha is >0.7. According to ref. [35], they stated that Cronbach’s Alpha value <0.5 has low reliability, Cronbach’s Alpha values 0.5–0.7 have moderate reliability, 0.7 to 0.9 have high reliability, and Cronbach’s Alpha values >0.9 have perfect reliability. Reliability can also be seen from Composite reliability. The acceptable composite reliability value is >0.7. The inner model is evaluated by looking at the R-square value and the effect size (f-square). According to [25], if the R-square value is 0.67, it indicates that it is strong, an R-square value of 0.33 indicates a moderate model and an R-square value of 0.19 indicates that the model is weak. In addition, the f-square value measures the effect of the independent variable on the dependent variable, which is independent of the influence of sample size. The results of the f-square calculation are given a rating to make it easier to read the interpretation of the f-square calculation. The f-square rating used is based on [26] if the value is ≥ 0.02, small; ≥ 0.15, medium; ≥ 0.35, large.

The hypothesis is tested by looking at the significance value. The model formed as a result of hypothesis testing is then tested by looking at the overall fit of the model. Quality indexed is used to measure model fit from Goodness of Fit (GoF). Apart from that, the descriptive statistics that have been carried out in this research are looking at the trends in answers to the results of the questionnaire survey. We looked at trends in respondents’ answers associated with demographic data. We calculate the frequency of answers, for certain demographic data variables to identify patterns. We determine the demographic data variables that we observe are the farmer’s recording habits and the farmer’s income level.

2.2 Hypotheses

Small-scale dairy farming involves various business processes that contribute to the production and sustainability of the dairy industry. These processes include milk production, feeding, breeding, housing, milking, waste management, and resource utilization [27, 28, 29]. Small-scale dairy farmers, who are predominantly smallholder farmers, play a significant role in milk production in developing countries [30]. They generate the majority of milk supply and contribute to household income, poverty alleviation, and economic growth [31]. However, small-scale dairy farming faces challenges such as scarcity of feeds and fodder, high costs of cattle, lack of technical knowledge, and decreasing interest of future generations. To address these challenges and promote sustainable small-scale dairy farming, interventions, and support are needed, including access to land, provender, subsidies, and awareness about the potential of small-scale dairy farming as a stable career opportunity. The application of information technology can be a solution to facilitate access to information and knowledge. Apart from that, information technology can make communication and education easier. However, the application of advanced technology cannot be implemented immediately. There needs to be consideration regarding the level of adoption. Previous studies have explained several findings related to indicators that influence information technology adoption (Table 1). Initially, the literature studied was research in the scope of agriculture in general, dairy farming, plantations, and other ruminants.

Table 1

Factors affecting IT adoption in agriculture

Factors	Sources
Education	[1,2,32,33,34,35,36]
Income	[2,34,35]
Access to credit	[2,34,35,38]
Access to extension services	[2,34,35,38]
Membership (Cooperative/Organization)	[2,35,36,37]
Age	[1,2,32,33,34,35,36,39]
Gender	[1,2,32,33,34,35,36]
Experience	[2,34,39]
Farm size	[1,12,33,35,37,38]
Distance to market	[35,37,39,40]
Fulltime farming	[37,39,41]
Herd size	[12,33]
Familiarity with computer/userskill	[1,33]
Perception (relative advantages/reward)	[1,33]
Belief	[1]
Training	[1]
Possibility customize	[1]
Interoperability (user friendly)	[1,12]
Cost	[1]
Complexity	[1]
Data ownership	[1]
Privacy and security	[1]
Data standard	[1]
Time consumption	[1]

Many studies reveal that education is the main aspect that influences technology adoption [1,2,32, 33, 34,35, 36]. A study in the United States found that increasing farm size as well as dairy operators having a college education were factors associated with the adoption of technology, management practices and production systems that affected productivity [33]. The study by Giua et al. [1] states that the driving factors for technology adoption are usability, land size, and level of education. In addition, technological adaptation can be improved by providing vocational training to business owners who start businesses at a young age [34]. Membership in cooperatives also has an important role in technology adoption. Cooperatives can promote the adoption of technology in production, thereby increasing productivity and income [37].

The factors that have been identified and accepted will be further processed as a basis for developing a model for measuring the level of technology adoption in smallholder dairy farms. The proposed technology adoption rate measurement model is based on the theoretical concept of the TAM. TAM is a model that can be used to analyse the factors that influence the acceptance of an information technology-based system. Tam was first proposed by Fred Davis in 1989 and there have been several revisions to the proposed model. The revision of the TAM model by Vekantesh and Davis in 1996 (Figure 2) was referred to in research by [42]. Alharbi and Drew [42] adopted TAM to see the desire of academic actors to use the Learning Management System. So, the research [42] tries to propose a technology adoption model that can be identified before the technology is fully implemented. Thus, the TAM model that is closest to the scope of the research is the model proposed in the research [42] (Figure 3). The model will be adopted as a framework with some adjustments according to the relevance of this research.

Figure 2

The Technology Acceptance Model (TAM) was originally developed by Fred D. Davis in 1989 and updated by Vekantesh & Davis in 2000. The image has been adopted from Vekantesh & Davis [43].

Figure 3

TAM in understanding academics’ BIU learning management systems [30].

The TAM model was adopted and adapted for preparing hypotheses and models for this research. TAM is used as the main framework, but the indicators used are adapted to the context of the dairy farming sector. Therefore, a literature study is carried out to identify various factors as indicators that influence the adoption of information technology that is relevant to research. Validation from experts is required for approval that the indicators of the results of the literature study are relevant, or irrelevant, and/or there are additional indicators recommended based on experts’ experience.

Based on the results of interviews with experts regarding the TAM model and its indicators, some experts say that external factors can be adapted to smallholder dairy farming. What experts recommend are external factors in the TAM model. External factors in TAM should be given consideration to see that extension support and cooperative membership affect the rate of technology adoption. In addition, experts also suggest that basic skills in using technology are also likely to affect the acceptance of farmers in using information technology to help their work.

The results of mapping the identified indicators into the TAM model are listed in Table 2. Thus, a hypotheses model is proposed based on modifications to the TAM model and adjustments based on expert recommendations created as an initial model of technology adoption in Figure 4. Demographic aspects identified in Previous research were also taken into consideration in data collection in this study. This research divided the level of adoption based on the five Linkert scales that were used for the survey. If the respondent answered strongly agree with a score of 5, it indicates that the respondent is more ready to adopt new technology compared to respondents who answered with a score below 5. Based on the Livestock and Animal Health Statistics Book – Ministry of Agriculture of the Republic of Indonesia in 2022, the number of small-scale or household-scale dairy farming businesses is mostly spread across the island of Java, namely more than 90% [44]. This research has conducted a survey by sampling the island of Java. This sample represents small-scale dairy farmers in Indonesia. An explanation about sampling was added so that it can be used as a consideration for generalization. Population data are based on data from the government of the Republic of Indonesia

Table 2

Factors and indicators of TAM in dairy farms

Factors	Indicators		Sources
PEU	Interoperability	A1	[1,12]
	Interactivity	A2	[42]
	Expertise	A3	[42]
	Flexibility	A4	[42]
	Learning	A5	[42]
	Requirements (Possibility Customize)	A6	[1]
	Experience using IT	A7	[1,33]
PU	Efficiency	B1	[42]
	Performance	B2	[42]
	Productivity	B3	[42]
	Effectiveness	B4	[42]
	Easiness	B5	[42]
ATU	Belief	C1	[1]
	Satisfy	C2	[42]
	Usability	C3	[42]
BIU	Planning	D1	[42]
	Intention	D2	[42]
	Interest	D3	[42]
	Relevancy	D4	[42]
DS	Information management	E1	[1,45]
	Technical operation	E2	[37,13,46]
	Social	E3	[46]
	Creative	E4	[46]
	Security	E5	[37,46]
	Critical thinking	E6	[46]
SUP	Access to extension services	F1	[2,34,35]
	Reward	F2	[1,22,33]
	Training	F3	[1]
	Access to credit	F4	[2,34,35,38]
	Membership Cooperation	F5	[2,35,37,40]

Figure 4

Initial model as hypotheses model.

3 Results

3.1 Statistic descriptive and demographic data

Based on the hypotheses model related to technology adoption, a research instrument prepared in a questionnaire was used to survey. The survey was conducted by sampling traditional dairy farmers in Java. The sampling technique is purposive sampling. Respondents were selected based on recommendations from cooperative managers, village managers, or heads of farmer groups. Respondents involved 116 traditional dairy farmers. Respondent demographics are given in Table 3.

Table 3

Demographic data

Demographic criteria	Amount
Gender
Man	48
Woman	68
Age (year)
<20	4
20–30	24
30–40	48
40–50	28
>60	12
Education
No school	0
Elementary school	52
Junior high school	48
Senior high school	16
Bachelor	0
Income per month (Million -IDR)
<1	20
1–3	68
2–3	16
3–4	8
>4	4
Milk production per day (liters)
<10	24
10–20	40
20–30	22
30–40	14
40–50	8
>50	8
Experience in dairy farm (year)
<10	24
10–20	48
20–30	28
30–40	12
>40	4
Farm area (m ²)
<20	20
20–30	16
30–40	24
40–50	24
50–60	8
60–70	20
>70	4
Number of cows
<3	12
3–6	64
6–10	32
>10	8
Distance to market (m)
<500	76
500–1,000	16
>1,000	24

Demographic data, such as gender distribution among participants, is crucial for understanding the generalizability and applicability of research findings. Knowing the number of participants per gender can shed light on whether the technology adoption behaviours observed in the study are representative across genders or if there are specific trends and preferences unique to one gender. This is particularly important in sectors like agriculture, where gender roles might influence access to resources, decision-making, and attitudes towards technology, thus impacting the effectiveness of technology adoption strategies.

Understanding income levels and current record-keeping habits is essential in this research as it provides insights into the economic background and operational practices of the participants. This information can help assess how economic factors and existing management practices influence the adoption of new technologies. For instance, income levels may affect the ability to invest in new technologies, while existing record-keeping habits could indicate the readiness to adopt digital tools for farm management. This context is crucial for tailoring technology adoption strategies to the specific needs and capacities of the farmers.

The answers from these respondents are then linked to demographic data related to income and recording habits that have been carried out by small-scale dairy farmers to carry out their farms. The results can be seen in Figure 5 and Table 4. Figure 5 shows the level of responsiveness of the response to answers to questionnaire survey questions that look at their readiness to adopt technology. Those who are better prepared to implement new technology are dairy farmers whose income is IDR 3–4 million per month. The dairy farmers in this group are quite established farmers and are still enthusiastic about improving their livestock business. However, dairy farmers in the highest income group actually had lower readiness scores. This can be because they are already in a comfort zone in their business so they avoid the risk of losses because processes have changed with the presence of technology. Apart from that, this research also found habitual patterns in carrying out daily records on small-scale farms. Records carried out traditionally include milk production, reproduction, feed, and medical records. The small-scale dairy farmers surveyed had varying record-keeping habits. Some people record everything, but some don’t record anything at all. The results show that the more complete the records are, the more enthusiastic dairy farmers are in adopting technology to help their work, and conversely, the fewer records they have, the more they feel that technology is not needed.

Figure 5

IT adoption level and income per month.

Table 4

Small-scale farmer record-keeping habits mapped to IT adoption level

IT adoption level	Recording
	Milk production	Reproduction			Feed	Medical record
	Milk production	Insemination (Estruss)	Birth	cow’s dry period	Feed	Medical record
1
2	√
3	√	√
4	√	√	√	√
5	√	√	√	√	√	√

This study also saw that based on level, the profiles of respondents at each level could also be studied. The survey results in this study showed that most of the respondents were at level 3. At level 3, most of the respondents were aware of taking notes. Recording was carried out including the results of milk every day and the time of artificial insemination. Recording is mostly done in books. Other recording media used are on sheets of paper and barn housing walls. At level 3, respondents have dairy cows with milking status with an average of three cows and an income of 1 to 3 million per month. In the second rank, respondents are at level 5. Level 5 is a respondent who also keeps records. Record keeping is more complete. Level 5, the farmer has not only recorded milk yield and reproduction but has also recorded feed and health care records (medical record). The ranking of the three respondents is at level 4. Level 4 is the respondent who recorded milk yield, time of artificial insemination, time of birth, and time of cows in dry period. Respondents at level 2 recorded only milk results. Meanwhile, respondents at level 1 are farmers who do not record.

3.2 Construct IT adoption model

The initial model was formed based on identifying latent variables and relevant indicators. There are 6 latent variables and 30 indicators. Table 5 shows the results of executing the loading factor or outer loading calculation. The relationship between factors and indicators is calculated to determine the strength of the relationship. The greater the outer loading value, the stronger the indicator makes up the factor. In addition, the strength of the relationship between factors (inner model), depicted using blue-to-blue notation with lines, is also calculated. How significant a factor is can influence other factors. The outer and inner model testing sections will present more detailed calculations.

Table 5

The results of initial model’s outer loading

Variables	Indicators	Outer loading
PEU	A1	0.704
	A2	0.888
	A3	0.843
	A4	0.882
	A5	0.867
	A6	0.857
	A7	0.811
PU	B1	0.844
	B2	0.780
	B3	0.613
	B4	0.933
	B5	0.858
ATU	C1	0.879
	C2	0.944
	C3	0.880
BIU	D1	0.870
	D2	0.963
	D3	0.958
	D4	0.878
DS	E1	0.799
	E2	0.882
	E3	0.921
	E4	0.747
	E5	0.919
	E6	0.755
SUP	F1	0.863
	F2	0.232
	F3	0.731
	F4	0.030
	F5	0.769

3.2.1 Outer model evaluation

Evaluation of the outer model looks at the value criteria for convergent validity, discriminant validity, and reliability tests. The validation values for the latent variables and indicators in the model are determined using convergent validity. The validation value between unrelated indicators will be seen using discriminant validity. Furthermore, Cronbach’s Alpha or Composite reliability value is used for reliability tests. Convergent validity looks at the outer loadings value, which shows the correlation between the indicator and the construct. An indicator with a low loading value indicates that the indicator does not work in the measurement model. We have expected a loading value >0.7. In the outer model, we know cross-loading. This value is another measure of discriminant validity. The expected value is that each indicator has a higher loading for the measured construct compared to the other constructs’ loading value. In the outer model, we know Composite Reliability. This value indicates internal consistency. That is, a high composite reliability value indicates the consistency value of each indicator in measuring its construct. The CR value is expected to be >0.7.

The results of the initial model execution will show the outer model value. Based on the values resulting from the initial model execution, several values in bold in Table 5 do not meet the requirement >0.7. In this research, two iterations were carried out to obtain a model with an outer loading value >0.7 for all indicators. The final results of the evaluated model show that four indicators were eliminated, namely productivity (B3), reward (F2), and access to credit (F4). These results state that the indicators related to Outer loading in the model that have been evaluated can be seen Figure 6.

Figure 6

Evaluated model.

3.2.2 Construct reliability analysis

This step measures the reliability of latent variable constructs. This value is seen from the Cronbach’s Alpha value. The Cronbach’s Alpha and Composite reliability values in this research are shown in Table 6.

Table 6

Cronbach’s alpha and composite reliability

Variables	Cronbach’s alpha		Composite reliability
PEU	0.929	Perfect	0.943	Reliable
PU	0.883	High	0.921	Reliable
ATU	0.884	High	0.929	Reliable
BIU	0.937	Perfect	0.956	Reliable
DS	0.936	Perfect	0.935	Reliable
SUP	0.710	High	0.836	Reliable

3.2.3 Inner model evaluation

The inner model is evaluated by looking at the R-square value and the effect size (f-square). The R-square value indicates the amount of variance in the construct described by the model. R-square is used to see the proportion or percentage of the independent variable’s ability to explain the dependent variable’s variance. The R-square value is also called the coefficient of determination, calculated by squaring the correlation coefficient (R) value. The R-square value and effect size (f-square) results can be seen in Tables 7 and 8.

Table 7

R-Square values

Variables	R-square	Rate
PEU	0.513	Moderate
PU	0.686	Strong
ATU	0.748	Strong
BIU	0.761	Strong
Average R-Square	0.677

Table 8

F-square results

Variable	F-Square	Rate
PEU → PU	0.694	Large
PEU → ATU	0.164	Large
PU → ATU	0.426	Large
PU → BIU	0.022	Small
PEU → BIU	0.173	Medium
ATU → BIU	0.174	Medium
DS → PU	0.075	Small
DS → PEU	0.139	Medium
SUP → PU	0.001	Small
SUP → PEU	0.134	Small

3.3 Hypotheses test

The suggested research model was evaluated by looking at the significance value. The significance value is obtained by resampling using the Bootstrapping method. Bootstrapping treats the observed sample as if it represents the population [47]. The significance value used is 5%; thus, the t-value taken as a reference is 1.96 and p-values <0.005. The results of hypothesis testing show that two hypotheses are rejected. Overall results are shown in Table 9. The values in bold in Table 9 do not meet the requirements so several hypotheses are rejected.

Table 9

Hypotheses testing results

#	Hypotheses		P-Values	T-Statistic	Status
H1	PEU positively affects PU of an Information Technology	PEU → PU	0.000	6,170	Accepted
H2	PEU positively affects attitudes towards using an Information Technology	PEU → ATU	0.000	3.776	Accepted
H3	PU positively affects attitudes towards using an Information Technology	PU → ATU	0.000	5.982	Accepted
H4	PU positively affects intention to behavioural use Information Technology	PU → BIU	0.208	1.259	Rejected
H5	PEU positively affects intention to behavioural use Information Technology	PEU → BIU	0.002	3.057	Accepted
H6	Attitude towards using positively affects BIU Information Technology	ATU → BIU	0.005	2.875	Accepted
H7	Digital positively affects PU	DS → PU	0.037	2.043	Accepted
H8	Digital positively affects PEU	DS → PEU	0.002	3.073	Accepted
H9	SUP positively affects PU	SUP → PU	0.751	0.305	Rejected
H10	SUP positively affects PEU	SUP → PEU	0.001	3.265	Accepted

3.4 Quality index testing

The model formed as a result of hypothesis testing must be tested by looking at the model’s overall fit. Quality indexing measures model fit from GoF. According to the study by Barrios et al. [39], if the GoF value is 0.10, then a small fit model, a GoF value of 0.25, means a medium fit model, and a GoF value of 0.36 indicates a large fit model. The GoF value is calculated by the root of the average communality value multiplied by the average R-square [48, 49]. The average communality calculated using the average of Average Variance Extracted (AVE) results is 0.740. The average R-squared value in this model is 0.677. Thus, the square root result of multiplying the Communality average by the R-square average produces a GoF value of 0.708. Based on the GoF value obtained, the Large Fit Model is formed. The final model is shown in Figure 7.

Figure 7

Information technology adoption model for smallholder dairy farmers.

4 Discussion

The results of this research analysis indicate that the factors that influence the adoption of technology for smallholder dairy farmers consist of external factors, namely DS and Supporting (SUP) and internal factors in the technology adoption model, namely Perceived Ease of Use (PEU), Perceived Usefulness (PU), Attitude Toward Usage (ATU) and Behavioral Intention to Use (BIU). The hypothetical model is structured to see the interrelationships between factors. The results of hypothesis testing show that SUP is not correlated with PU. This shows that the support given to farmers does not directly correlate with the performance of farmers. However, SUP remains positively correlated with PEU. PEU is represented by interoperability, interactivity, expertise, flexibility, learning, and using IT. This shows that good support for farmers will facilitate the application of new technology. This finding is in line with previous studies. Several studies explain the application of knowledge and technology-based agriculture which makes agricultural extension agents the main element in implementing digitalization. Knowledge in the field of agriculture and its extension communication network is an important component of an agricultural innovation system that has the potential for digital disruption [50]. Extensionists can act as data analysts and even expert software users. They also combine their knowledge of the agricultural context with data collected through smart technologies in agricultural knowledge [51]. Besides that, research [52] demonstrated that the co-design process supported agricultural extension agents to enhance their digital agency. The hypothesis regarding PEU and BIU also does not show a correlation. Farmers who already feel that they have a perception of the ease of use of technology do not necessarily mean that they will plan to use the technology directly. This is influenced by their belief in the use of technology that technology can help their work. The sustainability of the use of technology is also a problem in itself related to the trust of farmers in the use of technology. Even though the new technology presented is easy to use, certainty about the sustainability of this technology needs to be ensured. On the other hand, the ease of use of new technology is also a consideration for small-scale dairy farmers.

The lack of a direct influence of support on farmers’ perceptions of technology’s usefulness might point to gaps in how support is structured or communicated. This discrepancy suggests that the form of support provided may not effectively address the specific needs or concerns of farmers regarding technology or that there might be a misalignment between the support offered and the perceived benefits by the farmers. It highlights the need for a deeper understanding of farmers’ expectations and the context in which they operate, suggesting that support mechanisms need to be more tailored and responsive to the farmers’ actual experiences and perceived value of technological solutions.

The lack of a significant correlation between support (SUP) and PU in this study suggests that the type or quality of support provided to smallholder dairy farmers may not effectively enhance their perceptions of technology’s practical benefits. This could indicate that while support initiatives are in place, they may not be adequately tailored to address the specific performance-related needs or fail to demonstrate the direct impact of technology on farming outcomes. This misalignment could lead to underutilization of available technological resources, emphasizing the need for support services to closely align with the farmers’ operational realities and expectations to improve technology adoption outcomes.

These findings suggest implications for policy, training, and technology design. For instance, the lack of correlation between support and PU might indicate a need for more targeted or contextually relevant support mechanisms. This could imply that policymakers and extension services need to develop more personalized support strategies that directly address the perceived benefits of technology. The disconnect between ease of use and intention to use suggests that merely simplifying technology may not be enough. There may be underlying issues of trust, relevance, or perceived long-term value that need to be addressed through community engagement and education. This could involve creating more opportunities for farmers to see and experience the tangible benefits of technology in their operations before they decide to adopt it.

Given the complexities in technology adoption, another layer to consider is the social and cultural context within which small-scale dairy farmers operate. The effectiveness of support (SUP) and its relation to PEU could be significantly influenced by social norms, the collective experiences of the community, and the prevailing attitudes towards technology. This suggests that interventions aimed at improving technology adoption should not only provide technical support but also address social and cultural barriers. Encouraging a culture of innovation and technology acceptance among small-scale farmers could enhance the perceived value and ease of adopting new technologies.

This study reveals intriguing insights into technology adoption among small-scale dairy farmers, highlighting the nuanced relationship between income levels, record-keeping habits, and technology readiness. Income levels influence technology adoption, as wealthier farmers often have better access to capital and information but may not adopt due to risk aversion [53]. This research found that the farmers with moderate incomes show a higher readiness to embrace technology, possibly driven by their aspiration to advance their businesses. Contrastingly, higher-income farmers exhibit less enthusiasm, potentially due to contentment with their current status and apprehension towards new risks. Furthermore, comprehensive record-keeping correlates with a greater inclination towards technology adoption, emphasizing the importance of organizational practices in facilitating technological transitions. This nuanced understanding underscores the need for tailored approaches in promoting technology adoption, considering the diverse backgrounds and operational habits of small-scale dairy farmers. A study by Aker [54]indicates that good record-keeping practices can significantly aid in the adoption of new technologies by providing accurate data for decision-making.

5 Conclusion

The aspect of human resources is also an important concern in the digitalization process. Generally, smallholder dairy farmers are the ones most left behind by the digitalization process. Therefore, there is a need for digitization that pays attention to the beneficial aspects seen from the aspect of smallholder farmers. The level of technology adoption and readiness of human resources can be measured as a basis for developing strategies for digitalization. This research has identified the factors that influence the rate of adoption of new technology by smallholder dairy farmers. The results of this research analysis indicate that the factors that influence the adoption of technology for smallholder dairy farmers consist of external factors, namely, DS and SUP, and internal factors in the technology adoption model, namely, PEU, PU, ATU, and BIU. The hypothetical model is structured to see the interrelationships between factors. The results of hypothesis testing show that SUP is not correlated with PU. This shows that the support given to farmers does not directly correlate with the performance of farmers. However, SUP remains positively correlated with PEU. PEU is represented by interoperability, interactivity, expertise, flexibility, learning, and using IT. This shows that good support for farmers will facilitate the application of new technology. Apart from that, farmers with moderate incomes show a higher readiness to embrace technology, possibly driven by their aspiration to advance their businesses. Contrastingly, higher-income farmers exhibit less enthusiasm, potentially due to contentment with their current status and apprehension towards new risks. Furthermore, comprehensive record-keeping correlates with a greater inclination towards technology adoption, emphasizing the importance of organizational practices in facilitating technological transitions. This nuanced understanding underscores the need for tailored approaches in promoting technology adoption, considering the diverse backgrounds and operational habits of small-scale dairy farmers.

By offering a nuanced analysis of these factors, this study contributes significantly to the literature on agricultural technology adoption, particularly within the context of small-scale dairy farming. It presents a novel understanding of the barriers and enablers to technology adoption, advocating for more personalized and contextually relevant support strategies. Furthermore, this research provides valuable insights for policymakers, extension agents, and technology developers on designing and implementing technology adoption strategies that are not only technically viable but also socially and culturally acceptable. The model developed and the insights gained from this study pave the way for enhancing digitalization efforts in the agricultural sector, potentially leading to increased productivity and sustainability among small-scale dairy farmers.

sofiyanti.indriasari11@ui.ac.id

Acknowledgement

Thank you to Dr. Maria Wurzinger from The University of Natural Resources and Life Sciences, Vienna, Austria for supporting this research. And colleagues who have helped during this research process from College Vocational Studies – IPB University, Bogor, Indonesia.

Funding information: The APC was funded by the Directorate of Research and Development, University of Indonesia under Hibah PUTI 2023 (Grant No. NKB-567/UN2. RST/HKP. 05 00/2023).
Author contributions: All authors have accepted 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. SI: conceptualization, methodology, investigation, formal analysis, writing, and editing; DIS: conceptualization, methodology, supervision, validation, and review; YR: data collection, data input, analysis, and validation.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

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Received: 2024-01-11

Revised: 2024-04-28

Accepted: 2024-05-11

Published Online: 2024-06-07

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/opag-2022-0304

Keywords for this article

dairy farm business process; dairy farm technologies; farmers adoption of information technologies; farm management information system; information technology strategy; small-scale dairy farming

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