176 Unified theory of acceptance and use of technology model to understand farmer's readiness: Implementation of precision agriculture based on digital IoT monitoring apps in West Java, Indonesia Niken Larasatia Adelia Anissa Putrib Annisa S. Soemodinotoc Nadya Alyssad Okke Siti Shoofiyanie a,b,c,d,eTelkom Corporate University Center, Gegerkalong no 47, Bandung, Indonesia.  niken.larasati@itdri.id (Corresponding author) Article History ABSTRACT Received: 20 August 2024 Revised: 4 November 2024 Accepted: 18 November 2024 Published: 16 December 2024 Keywords Agricultural systems Digital transformation Extended unified theory of acceptance and use of technology (UTAUT2 and UTAUT3) Farmer readiness Internet of things Precision agriculture Technology acceptance model. This research investigates the readiness of farmers in West Java to adopt IoT monitoring applications through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The digital transformation has introduced precision agriculture, an advanced technology-based approach that enhances the monitoring of crop and farmer needs. Smart farming leverages the Internet of Things (IoT) and sensor technology to optimize complex agricultural systems, thereby increasing productivity while mitigating environmental impact. Utilizing soil and weather sensors to measure temperature, nutrients, and humidity, the study explores factors such as performance expectancy, facilitating conditions, personal innovativeness, habit, behavioral intention, and use behavior that will influence technology adoption within diverse farming communities. A deeper exploration through the lens of the UTAUT Model reveals that West Javan Indonesian farmers are prepared to utilize the monitoring apps. The factors that affect the Use Behavior (UB) of the farmers consist of their internal or personal characteristics, Habit (H) and Personal Innovativeness (PI), and the external factors that correlate with the app developer’s performance are Facilitating Condition (FC) and Performance Expectancy (PE). Habits from farmers for data recording and the personal innovativeness will increase the intention to use IoT monitoring apps. Contribution/Originality: This study pioneers the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) to evaluate farmers' readiness for precision agriculture using IoT monitoring apps in West Java, Indonesia. It integrates technology adoption theories with regional agricultural practices, offering innovative insights into digital transformation in farming. DOI: 10.55493/5005.v14i4.5258 ISSN(P): 2304-1455/ ISSN(E): 2224-4433 How to cite: Larasati, N., Putri, A. A., Soemodinoto, A. S., Alyssa, N., & Shoofiyani, O. S. (2024). Unified theory of acceptance and use of technology model to understand farmer’s readiness: Implementation of precision agriculture based on digital IoT monitoring apps in West Java, Indonesia. Asian Journal of Agriculture and Rural Development, 14(4), 176–183. 10.55493/5005.v14i4.5258 © 2024 Asian Economic and Social Society. All rights reserved. Asian Journal of Agriculture and Rural Development Volume 14, Issue 4 (2024): 176-183. http://www.aessweb.com/journals/5005 https://orcid.org/0000-0002-6045-1503 https://orcid.org/0009-0005-0930-1023 https://orcid.org/0009-0007-8618-8938 https://orcid.org/0009-0004-9111-515X https://orcid.org/0009-0002-5923-5616 mailto:niken.larasati@itdri.id http://www.aessweb.com/journals/5005 Asian Journal of Agriculture and Rural Development, 14(4) 2024: 176-183 177 1. INTRODUCTION Digital transformation has significantly impacted various sectors, including agriculture, where precision agriculture is a technology-based approach that monitors individual crop and farmer needs. This involves identifying and localizing crops, insects, and weeds, monitoring performance, and mapping plantings (Akhter & Sofi, 2022). Understanding the needs of smart farming is crucial for farmers to utilize data effectively. Smart farming uses technologies like IoT and sensors to optimise complex agricultural systems, increasing productivity and reducing environmental impact (Wicaksono, Suryani, & Hendrawan, 2022). IoT sensors help farmers identify specific field zones, reducing water use and chemical runoffs (Piramuthu, 2022). In the modern agricultural landscape, the integration of technology has led to transformative shifts in farming practices, enabling greater precision, efficiency, and sustainability. Research on agricultural IoT technology is extensive and intensive, focusing on sensor extension (Xu, Gu, & Tian, 2022). Farmers' readiness to adopt digital technology is crucial for smart farming. Indonesia's telecommunications development has accelerated, raising agriculture's Gross Domestic Product (GDP) by 3.64%. However, digital familiarity doesn't guarantee effective IoT sensor usage. Communication tools and internet networks are necessary enabling conditions. While existing research has often explored the barriers and facilitators of technology adoption in the farming sector, this paper offers a fresh perspective by comprehensively employing the interplay of 1) Performance Expectancy (PE), 2) Facilitating Condition (FC), 3) Habit (H), 4) Personal Innovativeness (PI), 5) Behavioral Intention (BI), and 6) Use Behavior (UB) of the UTAUT model. By delving into the intricacies of these multifaceted determinants, this paper contributes to a deeper understanding of obtaining the factors that influence the application of technology within diverse farming communities. In bridging the gap between theoretical insights and practical implementation, this research sheds light on novel pathways to foster precision agriculture development in an increasingly digital era. This research aimed to understand the farmers’ readiness to adopt IoT monitoring applications using a modified version of the Unified Theory of Acceptance and Use Technology (UTAUT) model to create precision agriculture. This paper addresses the question of how ready West Java farmers are to adopt monitoring applications and the internal and external factors that affect adoption readiness using the UTAUT Model. A key objective of the research is to study and gain insight into the factors influencing farmers' readiness to adopt Internet of Things monitoring applications in the context of precision agriculture. 2. LITERATURE REVIEW AND RESEARCH FRAMEWORK 2.1. Literature Review The UTAUT2 model has been successful in assessing factors influencing technology acceptance of different technologies with high explanatory power (Walle, 2022). The extended UTAUT-3 model was modified to understand farmers' behavior in Indonesia, especially in West Java, regarding gadget usage. The authors eliminated four constructs because they were deemed irrelevant to the current behavior of West Java farmers, who have not yet utilized digital monitoring applications, and the IT company is still developing the product. The model can be modified based on the study's context (Venkatesh, Morris, Davis, & Davis, 2003). Due to their lack of experience with digital monitoring IoT technology, farmers are unsure about its ease or pleasure of use. The IT company has not yet launched the product, developed a prototype, and decided on its price, making the farmers unaffected by significant constructs like SI. This uncertainty hinders their ability to influence the impact of digital monitoring apps on their operations. Therefore, the model of UTAUT was modified based on the existing West Java farmers’ behavior with six constructs as the latent variables: 2.1.1. Facilitating Condition (FC) Facilitating Condition (FC) is a degree that represents the consumers’ perceptions of the resources and support available to perform a behavior, such as the availability of technological resources and technical infrastructure. Several studies (Jahangir & Begum, 2008; Mital, Chang, Choudhary, Papa, & Pani, 2018; Sicari, Rizzardi, Grieco, & Coen- Porisini, 2015) have examined the effect of FC on consumers’ perceived ease of use. 2.1.2. Performance Expectancy (PE) Performance Expectancy (PE) is the measure of how effectively users can carry out specific tasks after adopting a technology (Venkatesh, Thong, & Xu, 2012). 2.1.3. Personal Innovativeness (PI) Personal Innovativeness (PI) in this context of research is defined as an individual’s perceived propensity or attitude that reflects their inclination to experiment independently with and adopt new developments in information technology (Schillewaert, Ahearne, Frambach, & Moenaert, 2005). 2.1.4. Habit (H) Habit (H) can be defined as the extent to which people tend to perform behaviors automatically because of learning (Limayem, Hirt, & Cheung, 2007). 2.1.5. Behavioral Intention (BI) Behavioral Intention (BI) is the motivational factor that influences a given behavior, and the stronger the intention to perform the behavior, the more likely the behavior will be performed. Asian Journal of Agriculture and Rural Development, 14(4) 2024: 176-183 178 2.1.6. Use Behavior (UB) Use Behaviour (UB) can be defined as the degree of individual commitment to using specific technology continuously. 2.2. Research Framework The research model proposed above was developed for a hypothesis based on West Java farmers’ present behavior and various supporting ideas. The author’s study hypothesis primarily concerns identifying technology adoption in West Java. The research-based hypotheses and their respective relationships: are listed below: H1: Facilitating conditions positively affect performance expectations. Facilitative circumstances motivate customers to use technology more easily, such as in mobile shopping services, boosting performance expectations and enhancing the overall shopping experience. H2: Performance expectancy influences behavioural intention. PE is the degree to which an individual believes that using technology would help them perform better in their work. PE has been identified as a component that influences BI. PE positively impacts the intention to use IoT technology (Ronaghi & Forouharfar, 2020). H3: Habit positively affects personal innovativeness. The transfer of learning models can help establish mobile payment usage habits by highlighting the similarity between two inventions and personal innovativeness (Agarwal & Prasad, 1998). This helps identify individuals who are more likely to adopt IT advancements. Highly innovative customers are more likely to accept new technology when they consider it superior to incumbent innovations, even if it's technologically complicated and unfamiliar. H4: Personal innovativeness positively affects people’s willingness to use digital IoT (UB). PI is a human quality that impacts individuals’ desire and openness to accepting new information technology innovations11. H5: Behavioural intention positively influences the use behaviour of digital IoT systems. A person’s BI reveals their mental preparedness to be persuaded to use technology (Venkatesh et al., 2003). Furthermore, the Theory of Planned states that a higher degree of perceived behavioral control correlates with a higher intention to use technology. An individual’s behavioral intention signifies their mental preparedness to adopt technology (Ronaghi & Forouharfar, 2020). Ultimately, the authors created a relationship between the original model and the best model of digital UB among Indonesian farmers, as shown in Figure 1. Figure 1. A proposed research model based on UTAUT for Indonesia farmers in West Java. Source: Farooq et al. (2017). The relation in Figure 1 was created specifically for this study. This research defines latent variables as a set of measurable variables that cannot be directly observed. These variables are inferred from the measured or observed variables, which are measurable and assigned to multiple questions to predict the latent variable in Figure 2. Therefore, the authors drew inspiration for the study's framework from Farooq et al. (2017) UTAUT-3. The theoretical framework proposed for this study can be seen in Figure 2. Asian Journal of Agriculture and Rural Development, 14(4) 2024: 176-183 179 Figure 2. Proposed research model with questionnaire items based on UTAUT for Indonesian farmers in West Java. 3. METHODOLOGY This study is conducted to understand the farmers' readiness to adopt IoT monitoring applications using a modified version of the UTAUT model. The proposed UTAUT model was validated through the quantitative method using surveys of 40 farmers in West Java. The primary products produced by the farmers who participated in the survey fall under the olericulture category. The farmers were chosen using a non-probability/non-random purposive sampling strategy to ensure they had prior experience with agriculture IoT devices, like weather and soil sensors; some of them were also the owners of the farm and could make the decision to adopt the technology. Non-probability purposive sampling, is a planned and focused strategy used in qualitative research to choose participants under the researcher’s discretion who can give in-depth insights and information relevant to the research issue (Neuman, 2014). The lack of details in empirical articles regarding the determination of sample sizes for SEM calculations often leaves the extent of sample-size planning unclear. Researchers frequently use rules of thumb, such as absolute minimum sample sizes or those based on model complexity. For SEM-PLS, a suitable sample size should meet one of two criteria: The sample size should be either (i) be at least ten times the largest number of items or questions used to measure a construct, or (ii) be ten times the largest number of paths associated with a construct. For the proposed UTAUT model, condition (i) requires a minimum sample size of 40 (Aparicio, Bacao, & Oliveira, 2016). There are two parts to the quantitative data collection. The first part collects information regarding the demography of the farmers. The second part is a 5-point Likert scale questionnaire used to validate the UTAUT model using the structural equation modeling (SEM) calculation. This part consists of 19 questionnaire items, each serving as an observed variable that corresponds with the UTAUT-3 constructs or latent variables. The model and questionnaire items were retrieved from Farooq et al. (2017) with some adjustments to fit our objectives. The data were analyzed using the Partial Least Squares (PLS) technique with SMARTPLS 3. Structural Equation Modeling (SEM) is a multivariate, hypothesis-driven technique based on a structural model representing hypotheses about causal relationships among variables (Stephan & Friston, 2009). Thus, to perform SEM-PLS, the UTAUT model must first be constructed according to the hypotheses concerning the relationships among variables. 4. RESULT AND DISCUSSION 4.1. Demographic Summary Result This study collected some demographic data to get the depiction of the respondents. Table 1 presents the summary of the data collected in the study. Asian Journal of Agriculture and Rural Development, 14(4) 2024: 176-183 180 Table 1. Demographic information for questionnaire respondents. Characteristics n (Respondents) % Gender Male 20 50 Female 20 50 Age < 25 years old 2 5 25-45 years old 18 45 > 45 years old 20 50 Last education No formal education 2 5 Primary school 21 52.5 Junior high school 7 17.5 Senior high school & equivalent 6 15 Diploma & bachelor degree 4 10 Years of farming experience < 5 years 9 22.5 5-10 years 6 15 10-20 years 16 40 >20 years 9 22.5 Total land area <1000m2 6 15 1000-5000m2 8 20 5000-10000m2 2 5 >10000m2 5 12.5 Electric device ownership Feature phone (SMS & call only) 8 20 Smartphone - Android 20 50 Smartphone - Apple 2 5 Laptop/PC 3 7.5 No device 11 27.5 The table reveals that many farmers, irrespective of their digital skills, share their devices with the family members such as children or partners. Many farmers, regardless of their digital skills, share their devices with family members, bridging the digital divide. The importance of digital access, particularly for those with lower literacy, motivates this practice. Farmers typically start their work in farming from primary school, with most relying on instinct and sense rather than data for farming activities. 4.2. SEM – PLS Calculation Result All calculations for SEM-PLS were processed using SmartPLS 3.0. To ensure the reliability and validity of the questionnaire items, construct validity and reliability tests were conducted by assessing Cronbach’s alpha (CA), average variance extracted (AVE), and composite reliability (CR). CA and CR measure the internal consistency of the questionnaire’s scale, while AVE evaluates the proportion of variance captured by a construct relative to measurement error. For the model to be considered reliable, CA and CR values should exceed 0.7, and the AVE should be above 0.5. Table 2’s reliability test result shows that all constructs have values of 0.7 and above for CA and CR, demonstrating the consistency of the questionnaire’s scale. Table 2. Construct validity and reliability. Variable CA CR AVE BI 0.823 0.894 0.739 FC 0.813 0.878 0.648 H 0.856 0.912 0.775 PE 0.892 0.925 0.757 PI 0.773 0.869 0.689 UB 1.000 1.000 1.000 The discriminant validity of the proposed model was assessed using the Fornell-Larcker criterion, which requires that the square root of the Average Variance Extracted (AVE) for each construct be higher than the highest correlation between that construct and any other construct in the model. The Fornell-Larcker criterion calculations for the proposed model are presented in Table 3, and the results meet the required standard. Asian Journal of Agriculture and Rural Development, 14(4) 2024: 176-183 181 Table 3. Calculatioin of the Fornell-Larcker criterion for the discriminant validity. Variable BI FC H PE PI UB BI 0.859 FC 0.650 0.805 H 0.440 0.747 0.880 PE 0.735 0.712 0.436 0.870 PI 0.736 0.833 0.790 0.722 0.830 UB 0.556 0.661 0.515 0.417 0.663 1.000 The study used 19 questionnaire items as observed variables to explain latent constructs’ value. The outer loading of these variables determined an item’s absolute contribution to its assigned construct. H_4 and FC_4 were found to be negative, indicating their contribution was insignificant and removed from the proposed model. 4.3. Hypothesis Result The P-value and path coefficient were calculated using the new proposed model, determining the significance of constructs in the relationship. A P-value less than 0.05 rejects the null hypothesis, while the path coefficient explains the direct effect of constructs. The P- value and path coefficient for the hypotheses mentioned in the research framework are shown with the proposed model in Figure 3. The hypothesis testing resulted in p-values of all relationships being below 0.05 (0.000 for H1 (FC → PE), H2 (PE → BI), and H3 (H → PI), 0.009 for H4 (PI → BI), and 0.003 for H5 (BI → UB)), indicating that all the relationships are significant, thus the hypotheses are accepted. Figure 3 shows the final model, the external loading of each variable to each construct, and the path coefficient of each construct’s relationship. Figure 3. UTAUT model for West Java farmer. The highest path coefficient value is observed between H and PI (H3) (0.790), suggesting that H strongly affects PI. 5. CONCLUSION In summary, after considering several factors, it is clear that the Indonesian farmers in West Java are ready to use the monitoring apps. The factors that affect the UB of the farmers consist of their internal or personal characteristics (H and PI) and external factors that correlate with the app developer’s performance (FC and PE). The results show that if the farmers perceive the FC to be in an ideal condition, this will help farmers create the PE to carry out specific tasks after adopting a technology (Venkatesh et al., 2012; Venkatesh, 2003) which will affect their intention to use the UB. Further, H had the highest path coefficient value, strongly affecting PI. Thus, creating a way for the farmers to develop a habit of recording agricultural data will help them carry out specific tasks after adopting a technology (Venkatesh et al., 2012; Venkatesh, 2003). Once farmers develop the habit of recording agricultural data, their use of the technology will increase, leading to an increase in their intention to use it, which in turn positively impacts the User Base (UB) of digital IoT monitoring apps. Asian Journal of Agriculture and Rural Development, 14(4) 2024: 176-183 182 6. LIMITATION AND FUTURE RESEARCH This study used a purposive sampling strategy due to the limitations of knowing the exact number of farmers who are familiar with or already use IoT monitoring apps and cross-sectional design, so there is limited scope to gather data only on farmers in West Java who were exposed to agriculture IoT sensors. Future research could incorporate some case-study-related variables into the UTAUT. Further, to make this research more comprehensive, the authors also suggested that future researchers continue this study by adding user acceptance testing (UAT) to obtain an in-depth understanding and gather the farmer’s feedback after launching the digital IoT monitoring apps. Funding: This research is supported by Telkom Corporate University Center. Institutional Review Board Statement: The Ethical Committee of the Telkom Corporate University Center, Indonesia has granted approval for this study (Ref. No. Tel. 01/ LB 000/TCU- A1050200/2024). 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Education background 5. Time of farming experience (In year) 6. Commodity 7. Land area size (m2 or hectare) 8. Ownership of electronic devices Agricultural data recording habits 1. Are you used to recording agricultural data? 2. Reasons for doing or not recording agricultural data? 3. What data is usually recorded? 4. How frequently is agricultural data recorded? 5. If there is a digital application to help farmers record data, would you be interested? (Yes/No) Please state the reason Performance expectancy (PE) PE_1 Digital IoT monitoring apps are very useful for my farming activities PE_2 Digital IoT monitoring apps help me to complete farming activities better PE_3 Using digital IoT monitoring apps increases the productivity of my farming activities PE_4 Using digital IoT monitoring apps helps me to produce better yields Facilitating conditions (FC) FC_1 I have a laptop to use digital IoT monitoring apps FC_2 I know how to use digital IoT monitoring apps FC_3 The digital IoT monitoring apps in my farmer group can be operated via my cell phone FC_4 The digital IoT monitoring apps provider provides assistance facilities when I encounter problems in its use Habit (H) H_1 I often use/Read graphs displayed in digital IoT monitoring applications H_2 I am used to using digital IoT monitoring applications H_3 The use of digital IoT monitoring applications has become a habit for me H_4 I am used to recording my agricultural activities manually in a notebook Personal innovativeness (PI) PI_1 I like to try new features and developments in the world of information technology PI_2 I am interested in trying the new features found in the digital IoT monitoring application PI_3 Usually, I am one of the first to adopt the latest monitoring applications among my colleagues Behavioral intention (BI) BI_1 I am interested in using digital IoT monitoring applications on my farm in the future BI_2 I will provide recommendations to other farmers to use IoT monitoring applications on their farms BI_3 I have positive expectations for my farm, when I use IoT monitoring applications Use behavior (UB) UB_1 How often will I use IoT monitoring applications on my farm Views and opinions expressed in this study are those of the author views; the Asian Journal of Agriculture and Rural Development shall not be responsible or answerable for any loss, damage, or liability, etc. caused in relation to/arising out of the use of the content.