Overview of new agricultural technology
In this study, the term ‘new agricultural technology’ primarily refers to precision agriculture technologies. Precision agriculture is a modern farming management concept that utilizes digital techniques to monitor and optimize agricultural production processes (Trivelli et al. 2019). These include advanced tools and methods such as: drones, automated tractors and satellite-guided equipment. Drones are pivotal in crop monitoring, enabling real-time assessment of crop health, disease identification, and precise application of inputs like fertilizers and pesticides (Das 2024). Automated tractors equipped with GPS and sensors optimize planting, plowing, and harvesting operations, leading to improved efficiency and reduced labor costs (Serrano et al. 2018). Satellite-guided equipment is also utilized for precision planting and irrigation, ensuring optimal application of seeds and water, resulting in better crop yields and resource conservation (Tsouros et al. 2019).
Precision agriculture has been rapidly adopted in rural areas of China, driven by government initiatives to modernize the agricultural sector and address challenges related to food security and sustainable farming practices. The implementation of precision agriculture technologies in China has enabled farmers to optimize resource utilization and improve crop yields, particularly in regions with extensive agricultural lands, leading to more efficient management practices (Kendall et al. 2017). Both public and private investments have been instrumental in supporting the development of precision agriculture in China, focusing on enhancing rural infrastructure, providing training programs for farmers, and integrating digital technologies into agriculture (Huang and Rozelle 2018). The ongoing progress of precision agriculture in rural China, primarily concentrating on staple crops like rice and wheat, is expected to further enhance agricultural productivity, mitigate environmental impacts, and elevate the livelihoods of farmers (Kendall et al. 2017). Technologies such as drones for crop monitoring, automated tractors with GPS, and satellite-guided equipment for precision planting and irrigation are pivotal in driving these positive changes in Chinese agriculture (Kendall et al. 2017).
The advancement of precision agriculture in China is rooted in technological innovations, shifting traditional farming practices towards more quantitative, localized, and technology-driven approaches (Feng 2024). This transition not only boosts agricultural productivity but also helps in reducing the negative environmental impacts associated with conventional farming practices (Kendall et al. 2017). As China continues to prioritize the development of precision agriculture, the sector is anticipated to play a crucial role in promoting sustainable agricultural practices, ensuring food security, and fostering rural development in the country.
Theoretical foundation and hypothesis development
The UTAUT aims to measure the factors influencing the acceptance and use of technology in various contexts. This model was developed by Venkatesh et al. (2003) by integrating several behavioral theories with four core and four control variables. The four core variables are social influence (SI), effort expectancy (EE), FC, and performance expectancy (PE). The four control variables are voluntariness, gender, age, and experience. The UTAUT model has been proven to be effective in evaluating the acceptance and use of technology in various studies in various contexts, including the insurance industry (de Andrés-Sánchez and Gené-Albesa 2023), digital library applications (Ali and Warraich, 2023), mobile banking (Samsudeen et al. 2022), information communication technology in tourism (Ali et al. 2022), early warning systems in higher education (Raffaghelli et al. 2022), online learning (Batucan et al. 2022), mHealth (Alam et al. 2018), and wearable payment device (Al Mamun et al. 2023).
In the UTAUT model, the primary research focus is on extrinsic motivation, particularly PE, which reflects the perceived functionality and practical value of the technology and has the greatest impact on users’ behavioral intention to adopt it (Venkatesh et al. 2003). Extrinsic motivation mainly refers to the extent to which the technology can meet users’ work requirements and improve efficiency. Later, Venkatesh et al. (2012) proposed UTAUT2, which introduced intrinsic motivation and hedonic motivation (HM), expanding the original model. In addition to the four core constructs of UTAUT (EE, PE, SI, FC), UTAUT2 incorporated HM, which refers to the enjoyment and pleasure users experience when using the technology, thereby shifting the focus beyond practicality to consider user experience. In summary, the core relationships in the UTAUT model suggest that PE, EE, SI, and FC influence users’ behavioral intention to use technology, while control variables moderate the impact of these core constructs. UTAUT2 further extended this by introducing HM, adding consideration of users’ intrinsic motivation. Based on this, the research model in this study introduces usage years and age as moderating factors to develop this framework. Furthermore, this research incorporated actual usage behavior instead of relying solely on user intention from the perspective of new agricultural technologies.
Intention to use new agricultural technology (IU)
Behavioral intention is defined as “the likelihood that a person may engage in certain behaviors and do something in the future under certain conditions” (Venkatesh et al. 2003). Intention to use new agricultural technology (IU) indicates the degree of intention to use and apply new agricultural technologies (Warshaw and Davis 1985). Studies have demonstrated that behavioral intention is a crucial factor in the acceptance of new technologies (Venkatesh et al. 2003; Kijsanayotin et al. 2009). In this context, IU denotes farmers’ intention to adopt new technologies for use in agricultural production.
Performance expectancy (PE)
PE, derived from the perceived usefulness of the technology acceptance mode, pertains to the degree to which an individual believes that technology can facilitate advancements in their work, such as enhancing efficiency (Ronaghi and Forouharfar, 2020). In research on new agricultural technologies, PE refers to the perceived effectiveness of new agricultural technologies in bringing about positive changes in various aspects of farming. This encompasses the anticipated benefits and improvements associated with the adoption and use of new agricultural technologies. PE is considered an influencing factor that directly determines a user’s behavior or intention (Shiferaw and Mehari, 2019). Farmers are often driven by a desire to improve their yield and overall farm output. If they believe that adopting new agricultural technologies will improve efficiency, save time, increase productivity, or enhance the quality of their crops, they will be more inclined to embrace these technologies. Previous studies (Quaosar et al. 2018; Gansser and Reich, 2021) have consistently shown a significant connection between behavioral intention to adopt healthcare technology and PE. Moreover, a recent investigation by Shi et al. (2022) presented evidence supporting the notion that PE significantly impacts the inclination to invest in agricultural technologies. Consequently, we propose the following hypotheses:
H1. Performance expectancy has a positive influence on intention to use new agricultural technologies.
Effort expectancy (EE)
An individual’s inclination to adopt a new technology is affected by its user-friendliness (Cimperman et al. 2016). In this context, EE pertains to how easily a farmer can employ new agricultural technologies (Sheng et al. 2016). Here, EE is influenced by perceptions related to the ease of cost management, learning, availability of time and energy, ease of transition, and suitability for operation. This reflects users’ expectations regarding how effortless or challenging it is to integrate and use the new agricultural technology. The rational connection between EE and intention to use new agricultural technologies is rooted in the concept that farmers are more inclined to adopt technologies when they perceive them as easy to use, learn, and integrate. This inclination is heightened when the necessary resources are available and there is a sense of suitability for operation. Scholars (Quaosar et al. 2018; Zhang et al. 2017) have revealed that EE serves as a reliable indicator of intention in various technology adoption perspectives as well as willingness to pay for agricultural technology (Shi et al. 2022). Therefore, we hypothesize the following:
H2. Effort expectancy has a positive influence on intention to use new agricultural technology.
Hedonic motivation (HM)
HM is characterized by the extent to which the utilization of a new technology results in satisfaction or pleasure and plays a pivotal role in both the adoption and use of the technology (Venkatesh et al. 2012). It delineates the respondents’ motivation to engage with the system or technology (Schukat and Heise 2021). Specifically, HM pertains to the positive motivation of an individual and exhibits a unique form of multidimensionality applicable to both monetary and non-monetary aspects (Uematsu and Mishra 2011). HM encompasses intangible benefits, such as joy, fun, entertainment, and other aspects beyond utilitarian factors (usefulness, efficiency, performance, etc.) (Shi et al. 2022). HM, arising from preferences, enjoyment of learning, social interactions, and resource availability, contributes to a positive attitude and, consequently, a greater inclination to embrace and use new agricultural technology. Farmers are more inclined to use new agricultural technologies if they find them interesting and enjoyable (Alam et al. 2020; Hew et al. 2015). Thus, we hypothesize the following:
H3. Hedonic motivation has a positive effect on intention to use new agricultural technologies.
Social influence (SI)
SI is defined as “the importance and influence of people who are close to or important to the person who can persuade him or her to accept a new technology or measure” (Venkatesh et al. 2003). In this study, it denotes the extent to which social opinions and the perspectives and practices of influential individuals affect farmers’ adoption of new agricultural technologies. According to Cao (2020), reference groups encompass individuals or collectives who play a pivotal role in comparing people based on their attitudes, intentions, and behaviors. The opinions and perspectives of peers and esteemed individuals can significantly sway an individual’s preferences and decision-making processes (Wei et al. 2019), especially when these viewpoints align with perceived usefulness and ease of use (Rajak and Shaw 2021). Research suggests that social circles within the workplace have a substantial influence on shaping attitudes and perceptions by acting as facilitators or catalysts for technology adoption (Jedwab et al. 2022; Ljubicic et al. 2020; Rajak and Shaw 2021; Yadav et al. 2022). Building on prior studies, Sun and Jeyaraj (2013) concluded that SI is a crucial factor promoting the behavioral intention to adopt new technologies. Therefore, we hypothesize the following:
H4. Social influence has a positive effect on intention to use new agricultural technologies.
Facilitating conditions (FC)
FC include “users’ attitudes towards organizational infrastructure and financial and technical support after acceptance of a new technology or measure” (Venkatesh et al. 2003). In this study, the FC component denotes the organizational or technical resources and facilities that are readily available to make the process of adopting a new agricultural technology easier for farmers. It delineates farmers’ perceptions of the extent to which the organizational and technological infrastructure in agriculture supports the adoption of new agricultural technologies (Schukat and Heise 2021). Bhattacherjee and Hikmet (2008) and Boontarig et al. (2012) emphasize that FC are influential factors in shaping users’ behavioral intentions toward adopting new technologies. Alam et al. (2020) discovered that FC have a positive impact on behavioral intention. The researchers emphasized the significance of FC, considering them to constitute a crucial factor determining a consumer’s intention to adopt technology, as highlighted by Dwivedi et al. (2016). Hence, we propose the following hypothesis:
H5. Facilitating conditions have a positive effect on intention to use new agricultural technologies.
Price value (PV)
PV is an additional element incorporated into UTAUT 2 (Venkatesh et al. 2012). It is characterized by the consumer’s perceived balance between the perceived system value and the cost associated with acquiring or using a new technology (Venkatesh et al. 2012). When utilizing new technologies, end users consistently evaluate the costs associated with the potential savings they may accrue from adopting those technologies (Baabdullah 2018; Alalwan 2020; Verdouw et al. 2016). With the use of new agricultural technologies, farmers’ agricultural production will obtain productivity gains at a more favorable price than traditional agricultural production (Venkatesh et al. 2012). There is evidence supporting the significance of FC as a crucial factor in farmers’ behavior when agricultural production adopts IoT (Shi et al. 2022). In this study, we hypothesize that the benefits of adopting new agricultural technologies for production outweigh their costs. Therefore, we hypothesize the following:
H6. Price value has a positive effect on intention to use new agricultural technologies.
Usage of new agricultural technology (UN)
The utilization patterns of new agricultural technologies refer to the actual degree of farmers’ application of new agricultural technologies. Behavioral intention serves as a reliable factor for predicting behavior (Davis 1989; Fishbein and Ajzen 1977; Ajzen 1991) in many behavioral theoretical models. Venkatesh et al. (2003) conducted research on the adoption of information technology by users and indicated that use behavior is directly influenced by FC. In a separate investigation, Raza et al. (2021) discovered a positive correlation between FC and BI. Additionally, Boontarig et al. (2012) proposed in their research that FC has a positive effect on BI and behaviors related to smartphone use for health services. Likewise, if farmers have positive intentions, it is reflected in their motivation and readiness to adopt and use new agricultural technologies. The expectation of positive outcomes acts as the driving force. Many studies have proven that the greater the intention people have to use a technology, the more likely they are to actually use it (Ronaghi and Forouharfar, 2020; He et al. 2020; Ling Keong et al. 2012; Venkatesh et al. 2003). Therefore, we hypothesize the following:
H7-8. Facilitating condition and intention to use new agricultural technology has a positive effect on farmers’ usage of new agricultural technologies.
Moderation of age and using years
Different age groups have varying levels of experience, adaptability to technology, and perspectives on innovation. Younger farmers may be more tech-savvy and open to adopting new technologies, while older farmers may rely on traditional methods. Farmers with more years of experience using technology may have developed an improved understanding of the advantages and challenges associated with technological adoption. Their experiences may influence how they perceive and utilize new agricultural technologies.
Younger farmers or those with fewer using years may be more influenced by the perceived benefits of technology, whereas older farmers with more experience may rely on their past experience. Younger farmers may adapt more easily to new technologies, whereas older farmers may find it challenging to change their established practices. Similarly, younger farmers might be more motivated by the enjoyment and satisfaction derived from using technology, whereas older farmers may prioritize practical benefits. Younger farmers may be swayed more by peer trends and community dynamics, whereas older farmers may depend on their established networks. Likewise, younger farmers or those with fewer using years may be more sensitive to the cost-effectiveness of technology, whereas older farmers may prioritize reliability over cost. In addition, farmers with more using years may have encountered various FC and learned to navigate them, whereas younger farmers may rely more on external support.
According to Nikolopoulou et al. (2021), the effects of FC on technology usage may differ by age, with younger farmers being more receptive and responsive to new conditions, whereas older farmers may require more convincing or support. Additionally, the connection between FC and technology usage is influenced by the number of years a farmer has been using technology, and farmers with more experience may have developed a greater ability to leverage FC (Nikolopoulou et al. 2021). Therefore, age and using years can moderate the link between these factors and the UN, reflecting farmers’ varying perspectives, experiences, and priorities at different stages of their careers and technology adoption.
H9a-f. Farmers’ age moderates the relationship between performance expectancy, effort expectancy, hedonic motivation, social influence, price value, facilitating conditions, and intention to use new agricultural technology as well as between facilitating condition and use new agricultural technologies.
H10a-f. Using years of technology moderates the relationship between performance expectancy, effort expectancy, hedonic motivation, social influence, price value, facilitating conditions, and intention to use new agricultural technology as well as between facilitating condition and use new agricultural technologies.
Mediation of intention to use new agricultural technology
IU acts as a central cognitive factor that integrates the influences of PE, EE, HM, SI, and FC. Farmers who perceive technology as beneficial (PE), easy to use (EE), emotionally satisfying (HM), socially endorsed (SI), cost-effective (FC), and FC are more likely to form a positive intention to use the technology (Shi et al. 2022, Venkatesh et al. 2012). This positive intention, in turn, becomes a strong predictor of actual technology usage. Farmers with a favorable inclination are more inclined to translate that intention into action by adopting and using new agricultural technology on their farms (Ronaghi and Forouharfar, 2020; He et al. 2020). Therefore, we hypothesize the following:
HM1-6. Intention to use new agricultural technology mediates the relationship between performance expectancy, effort expectancy, hedonic motivation, social influence, price value, facilitating conditions, and use of new agricultural technologies.
All association hypothesized above are presented in Fig. 1.
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