Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

doi:10.21203/rs.3.rs-2430465/v1

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Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

https://doi.org/10.21203/rs.3.rs-2430465/v1

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published 26 May, 2023

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Smart agricultural (SA) technology has become an inevitable trend in the modernisation of China's agriculture and a realistic demand for the transformation and upgrading of traditional agriculture. Based on microscopic research data, a structural equation model (SEM) is constructed using deconstructive planning behaviour theory (DPBT) as the analytical framework to empirically analyse the factors and degrees of influence on cotton farmers' adoption of SA technologies. In-depth interviews were then combined to further reveal the mechanisms influencing cotton farmers' adoption of SA technology. The results show that: (1) the risk of the technology itself has a dampening effect on willingness, but cotton farmers value the positive role played by perceived usefulness more. (2) superior influences such as trial field demonstrations and government publicity have a greater impact on willingness to adopt SA technologies than the group effect of peer influence. (3) factors such as self-efficacy and information channels have an impact on willingness and behaviour to adopt. (4) Willingness influences technology adoption behaviour through mediating effects. (5) Policy and technology satisfaction have a positive moderating role in the occurrence of willingness and adoption behaviour.

Biological sciences/Psychology

Earth and environmental sciences/Environmental sciences

Earth and environmental sciences/Environmental social sciences

With the introduction of climate-smart agriculture (CSA), it has received widespread attention from scholars around the world^1–3. SA has been commonly applied in the field of agriculture, relying on various SA technologies. SA has become a global trend in the development of agricultural modernisation⁴, and developed countries have objectively formed a squeeze and control on the development of agricultural industries in developing countries by using high-level agricultural technology and agricultural subsidy support as a carrier³ and capital flow and market expansion. Therefore, if China wants to safeguard the steady development of agriculture under the constraints of resources and environment, it must change the way of agricultural development, take the path of high-quality agricultural development, and hold the initiative of agricultural development firmly in its own hands, so as to achieve the transformation from a large agricultural country to a strong agricultural country. For more than a decade in a row, China has published documents related to SA, which is an important initiative to break through the current bottleneck of traditional agricultural development⁵, achieve high-quality, high-efficiency and sustainable development in agriculture^6,7 and take the lead in modernising agriculture and rural areas.

The ageing of those currently engaged in agriculture remains a serious problem⁸, which can significantly weaken the level of human capital in agriculture, severely limit the scope for technology diffusion and become a potential threat to technological transformation in agriculture⁹. In the reality of high production costs and risks, there is also a need to improve production efficiency through smart agricultural technologies. Secondly, with the rise in cotton prices and subsidy policies, cotton farmers are stimulated to demand a higher standard of agricultural machinery. In addition, the inefficient use of arable land in current agricultural production and the excessive use of water and agricultural resources in agriculture further exacerbate the overexploitation of resources^10,11. At the same time, excessive inorganic inputs have led to a decline in the quality of agricultural products, a decline in land strength, agricultural surface pollution and ecological degradation. Such a reality indicates that there is an urgent need to further liberate labour, improve productivity and conserve resources, and raise farmers' incomes and modern skills, supported by smart farming technologies¹², in order to enhance the endogenous development of rural areas. And farmers, as implementers, have a direct influence on their adoption behaviour.

The current research on SA mainly includes the definition of the concept and characteristics of SA, the development status of SA, problems and countermeasures and suggestions^13–15. The core technologies in SA are explored in depth, mainly focusing on the application of agricultural production as well as operation and management¹⁶, such as the analysis of the application of SA technologies such as the Internet of Things, big data and satellite remote sensing technology^17–19, as well as typical development models and practices of SA at home and abroad²⁰.

Based on the classical "economic man hypothesis", most scholars regard farmers as rational economic men and believe that farmers' technology adoption behavioural decisions are rational and economic^21,22. However, farmers' behaviour deviates from economic rationality due to their personal characteristics and subjective biases in the decision-making process of behavioural response. As a result, the content and scope of research has been expanding. In-depth studies have been conducted from different perspectives and for different purposes. Showing that farmers' behavioural decisions can be influenced by a combination of internal and external factors has deepened the understanding of farmers' behaviour and the logic behind it. Farmers' gender^23–25, age, herd mentality, social capital²⁶, soil fertility and farm size²⁷ are the underlying factors ²⁹ that lead to differences in farmers' perceptions²⁸. The larger the land size, the more effective the adoption of new technologies to achieve efficient production ³⁰ and the more motivated farmers are to adopt them²⁶. Behaviour is also influenced by a combination of other individual characteristics such as literacy, part-time status, access to information, risk perception, social networks³¹ and household characteristics ^32,33. Resource endowment constraints^{19, 34–36}, which are mainly composed of land, labour and capital, are one of the key factors influencing the technology adoption behaviour of farm households. Smallholder farmers are subject to resource constraints that can be effectively mitigated through agricultural social services³⁷ and promote farm technology adoption³⁸. External factors revolve around factors such as policy environment and risk preferences. Government subsidies and policy regulation serve as the two main incentives adopted by governments. Subsidy policies provide incentives for farmers to shift to planting segments that require new technologies more^39,40, while institutional constraints discourage farmer adoption behaviour¹⁵. The government also motivates farmers through agricultural technology training^41–42, advocacy and guidance, and monitoring and control⁴³. In addition, agriculture is naturally vulnerable to multiple risks from natural disasters and market changes⁴⁴, and it is important for farmers to take into account the possible risk factors when adopting a new technology so that they can allocate their available capital endowment efficiently.

At this stage, the research has been fruitful, but there is still room for expansion. Most of the research subjects are about technologies such as green, low-carbon and CSA, and there is still a need for scientific analysis and accurate pulse on the state of SA technology adoption. In terms of research theory, there is an increasing number of studies exploring technology adoption from the perspective of farmers, with most scholars using the Theory of Planned Behaviour (TPB) to study the influencing factors and pathways^45–47. Even though the theoretical system of this framework is more complete and consistent, the selection of indicators is still based on the original structure, considering too many single factors and lacking deeper exploration; research methods mostly make use of models such as Logistic²¹ and Probit³¹.

Based on this, this paper uses SEM based on the DTPB to analyse in depth the underlying mechanisms and pathways of each influencing factor. In addition, the current research on technology choice is rather general, and there are still some farmers who are unwilling or fail to adopt new technologies, and what are the reasons for this? Therefore, this paper attempts to dig deeper into the dynamics of cotton farmers' adoption behaviour and their influencing mechanisms from a micro perspective, and to analyse the behavioural logic of cotton farmers' adoption of SA technologies, in order to provide a reference for effectively facilitating cotton farmers' adoption of SA technologies and to provide new ideas for research related to SA.

The TPB theory was re-integrated and refined by multi-dimensional decomposition again to form the DTPB. the DTPB theory possesses reliability and rationality, with a stronger structure and more precise estimating power, and is more suitable for different categories of research⁴⁸. It also investigates the deeper psychological perceptual elements of individual behaviour. Based on this, this paper draws on this theoretical model to delineate the psychological factors underlying cotton farmers' adoption of SA technologies at a deeper level across three scales: behavioural, normative and control beliefs. In addition, policy and technology satisfaction are added as moderating variables to explore the moderating effects in the transformation of intentions into behaviour (Fig. 1).

Behavioural beliefs refer to cotton farmers' positive or negative attitudes towards the adoption of SA technologies and are determinants of attitudes. In adopting new technologies, cotton farmers consider the immediate benefits and the long-term future development that they will bring⁴⁹. In addition, the main concern of cotton farmers is that gaining benefits and risks are in conflict with each other. Not only does the adoption of new technologies bring benefits, but there is also risk taking and avoidance. As technology implementation is subject to a variety of impacts, it results in risky decisions under conditions of technological uncertainty of adoption. The more cotton farmers perceive the technology, the more positively they evaluate its convenience and effectiveness, the stronger their willingness to adopt it. Behavioural beliefs are formed when cotton farmers perceive that SA technologies are driving or hindering change. In this paper, behavioural beliefs are decomposed into two antecedent variables: perceived usefulness and perceived risk. When the benefits carried by SA adoption are higher, cotton farmers' attitudes towards SA adoption will be higher, thus promoting behavioural intentions and behaviours; at the same time, when the risks carried by SA adoption are lower, cotton farmers' attitudes will be more proactive, resulting in increased willingness to adopt and higher adoption behaviours.

Normative beliefs refer to the external pressure cotton farmers feel to adopt and reflect the influence of institutions, organisations or systems on individual decisions. If a cotton farmer is uncertain about the outcome of a SA technique, he or she may choose to listen to the opinions of others in order to judge his or her own behaviour. And they can feel the insights or ideas relayed to them by nearby family members, friends and relatives. But cotton farmers are not only attached to a network of social relations at the level of their peers, they also rely on a network of social relations at the level of their superiors. In social group interactions, cotton farmers often deal with villages or regiments and agricultural dealers, and government promotion efforts have an impact on the probability of technology adoption and the degree of adoption by cotton farmers¹⁷. The government has made cotton farmers more enthusiastic about adopting new technologies through unified learning by heavily promoting SA technology and organising training on the technology¹⁸. At the same time, the price subsidy policy granted by the government will stimulate cotton farmers' willingness to adopt the technology. In this paper, peer influence is split into family and close friends. The promotion efforts, publicity and subsidy mechanism of the village or regiment and local government are screened as indicators of superior influence. When social groups and superior relationships motivate cotton farmers to adopt, the greater the willingness and behaviour of cotton farmers to adopt.

Control beliefs refer to the degree of difficulty and mastery cotton farmers perceive in adopting SA technology. It covers both self and external dimensions: firstly, the cotton farmers' perception of their own capabilities, and the formation of willingness also varies according to self-tolerance⁵³. Secondly, external influences on one's own behaviour; external forces refer to the extent to which the relevant resources at the cotton farmer's disposal facilitate adoption. The behavioural choices of cotton farmers depend on their subjective intentions. The stronger the subjective will of the cotton farmers, the more they will take the initiative to obtain information in various ways and thus adopt it more quickly. In addition to being available in villages or missions, there are also online sources that can be accessed⁵⁴. For example, tools such as the government's online agricultural extension service platform, the ShakeOut platform or public numbers can be used to provide online training, browse agricultural policies or explain agricultural knowledge directly through mobile phones^55,56. Broadening cotton farmers' access to information channels, enhancing the timeliness and effectiveness of access to information content, grasping the latest agricultural technology adoption dynamics, narrowing the digital information gap, reducing information acquisition costs, facilitating timely agricultural decision-making by cotton farmers, and ultimately enhancing technology choices⁵⁷. At the same time, the more resources cotton farmers believe they have and the less hindered they are, the higher their willingness to adopt. We split control beliefs into three antecedent variables of self-efficacy, resources and technology facilitation, and selected indicators of autonomous decision-making power, mastery, risk and time cost tolerance, smooth flow of information in village groups, and online information platforms. When cotton farmers have a higher degree of recognition of their own abilities and believe that they can afford the process and outcome of adoption, the stronger their willingness and behaviour to adopt will be; at the same time, when the facilitation conditions are more favourable, the more they can promote the adoption of SA technology.

As individual SA technologies are not yet widespread and there is an information asymmetry, cotton farmers do not necessarily translate into behaviour even if they have the will to adopt SA technologies. Government policy, as an important tool and measure of national macro-control, is an enabler of transformative production and development in China's agriculture. The behaviour of cotton farmers in adopting SA technologies has positive externalities and requires the support and guidance of government policies, and the satisfaction of cotton farmers with these policies will affect the transformation of willingness into actual behaviour. In addition, when friends, relatives, demonstration households and other growers adopt SA technologies and achieve benefits, the demonstration effect and imitation effect will be generated, and the satisfaction of cotton farmers with SA technologies will enhance the change from willingness to behavioural adoption. Therefore, this paper introduces policy and technical service and effect satisfaction as moderating variables to investigate their moderating effects on behavioural willingness to adopt SA technology.

Data collection. China is the world's largest cotton producer, and Xinjiang is the main cotton-producing region in China. After the school has opened a research letter. The survey was conducted from July to September 2022. Relevant research was conducted in the research area prior to the survey and communication was made with the local agricultural authorities in advance. After obtaining verbal consent from government departments, the time to go to the area for the survey was confirmed and basic information on farmers was provided to the researchers. To ensure the representativeness and diversity of the data, the survey selected cotton farmers of different types and business scales from the main cotton-producing areas in northern Xinjiang: Changji, Hutubi and Manas counties in Changji Prefecture, Bole City in Bo Prefecture and the 6th and 8th Divisions of the Xinjiang Production and Construction Corps for questionnaire surveys and household interviews. After obtaining informed verbal consent from the participants prior to the research, a total of 400 questionnaires were distributed and 394 valid questionnaires were collated, with an effective rate of 98.5%.

Measurement. Due to the small scope of application of some of the SA technologies, in order to ensure the accuracy of the data, the assignment of B1 in the study using SA technology variables was scored according to the degree of adoption of five technologies: GPS positioning system seeding integrated machine, unmanned aircraft, water and fertilizer integrated intelligent drip irrigation system, cotton field environmental monitoring control system, and Beidou navigation system of baling cotton picking integrated machine. Running SEM and mediating effects using AMOS 28.0 software. The moderating effect analysis was analysed using SPSS 28.0 software, and the moderating variables were selected to score cotton farmers' satisfaction with the policy and technology use effects and services of each of the five smart farming technologies, with the mean value taken at the end. All other variables were assigned using a five-point Likert scale. Where 1 - completely disagree, 2 - not very much agree, 3 - average, 4 - basically agree and 5 - completely agree. The survey was set up to measure a total of 38 question items and 14 latent variables (Table 1). All methods involved in this study were conducted in accordance with the guidelines and regulations of the Humanities and Social Sciences Research Ethics Committee. And all experimental protocols are approved by this committee.

Table 1

Variable setting and reliability validity testing
Variable	Code	Observed variable	AVE	Std.
Perceived usefulnes	PU1	SA technology can increase economic returns	3.596	0.788
	PU2	SA technology can increase yield	3.556	0.824
	PU3	SA technology can benefit the cotton industry	3.307	1.288
	PU4	SA technology can reduce labor input and save energy and physical strength	3.249	1.248
	PU5	SA technology can reduce water consumption, pesticide and fertilizer use	3.406	0.851
Perceived risks	PR1	The adoption of SA technology is ineffective and there are risks of immature technology	2.282	0.777
	PR2	SA technology can increase costs	2.751	1.075
	PR3	Lack of follow-up guidance or maintenance by technical staff after using SAtechnology	2.409	0.789
Peer influence	PI1	My family supports me to adopt SA technology	3.594	0.933
Peer influence	PI2	My family supports me to adopt SA technology	3.693	0.977
Superiors influence	SI1	Villages/crops support the adoption of SA technology	3.188	1.008
	SI2	Agricultural distributors promote the adoption of SA technology	3.305	1.119
	SI3	Government policies to promote SA technology	3.561	1.125
	SI4	Government subsidies for the acquisition of SA machines	3.371	1.076
Self-efficacy	SE1	It is up to me to decide whether to adopt SA technology or not	3.566	1.214
	SE2	I am capable of learning and mastering SA technology	3.497	0.758
	SE3	I can bear the risks associated with adopting SA technology	3.340	1.104
Resource facilitation	RF1	My family has enough financial support to invest in SA technology	3.046	0.866
Resource facilitation	RF2	I have enough time to learn SA technology	3.609	0.843
Technology facilitation	EF1	Information channels for SA technology are available in the village/crops	3.584	0.770
Technology facilitation	EF2	Web-based information platform to promote SA technology	3.622	0.791
Behavioral attitude	BA1	I think the advantages of SA technology outweigh the disadvantages	3.553	1.150
	BA2	I think it is necessary to adopt SA technology	3.893	0.808
	BA3	I think SA technology is a future development trend	3.891	1.261
Subjective norms	SN1	People who have influence on my family agree with the adoption of SA technology in my family	3.655	0.970
Subjective norms	SN2	Relevant systems and guarantees support the adoption of SA technology in my household	3.761	0.934
Perceptual behavior control	PBC1	I have the conditions to adopt SA technology	3.538	0.709
	PBC2	I have easy access to information about SA technology	3.734	0.766
	PBC3	I can easily adopt SA technology	3.505	0.788
Behavioral willingness	BW1	Willing to adopt SA technology	3.746	1.111
	BW2	Willing to attend training on SA technology	3.754	1.202
	BW3	Willing to recommend people around me to use SA technology	3.718	1.175
	BW4	Willing to invest time, money and adopt SA technology	3.406	0.854
Behavior	B1	Adoption of SA technologies	3.708	0.776
	B2	Level of knowledge about SA technologies	3.431	0.816
	B3	Proactive participation in training on SA technology	3.622	0.896
Technology satisfaction	TS	Satisfaction with the effectiveness of using SA technologies and services	3.182	1.184
Policy satisfaction	PS	Satisfaction with policy onSA technology	3.063	1.229

Behavioural attitudes have a significant influence on cotton farmers' willingness to adopt SA technologies, followed by subjective norms and perceived behavioural control. Furthermore, the greatest degree of influence on cotton farmers' adoption of SA technology was behavioural willingness and the least was perceived behavioural control. The path coefficient of perceived usefulness to behavioural attitudes in behavioural beliefs was 0.426, which was higher than the path coefficient of perceived risk − 0.353. This indicates that in adopting SA technology, cotton farmers are more concerned about the important role of changes in welfare levels brought about by SA technology itself, even though the presence of partial risk inhibits their intention to adopt SA technology. The standardised path coefficients of superior influence and peer influence on subjective norms in the normative beliefs were 0.385 and 0.316 respectively, indicating that cotton farmers are more dependent on their superiors in the decision-making process. The path coefficients of self-efficacy, resources and technological facilitation on perceived behavioural control in control beliefs were 0.27, 0.185 and 0.315, respectively, indicating that technological facilitation had a stronger influence on cotton farmers (Fig. 2).

Mediating effects. In order to further verify the mechanism of the role played by cotton farmers' willingness to adopt SA technology, the Bootstrap method was used to analyse the impact of the mediating effect under different paths. Table 2 shows that the corresponding confidence intervals for each path do not include 0, indicating that the mediating effects of the different paths are significant and all have a partial mediating effect. The standardised direct effects for the three different paths between behavioural attitudes, subjective norms, perceived behavioural control, willingness to adopt and behaviour were 0.184, 0.184 and 0.171 respectively, the indirect effects were 0.105, 0.089 and 0.081 respectively, and the total effects were 0.288, 0.273 and 0.252 respectively.

Table 2

Results of mediating effect test (bootstrap = 5000)
Path	Effect		SE	Bias Corrected(95%)				Percentile method(95%)
Path	Effect		SE	LLCI	ULCI	P	LLCI		ULCI	P
Behavioral attitude→Behavioral willingness→Behavior	Direct Effect	0.184	0.066	0.054	0.314	0.006	0.054		0.314	0.006
	Indirect Effect	0.105	0.029	0.056	0.171	0.000	0.053		0.168	0.000
	Total Effect	0.288	0.064	0.162	0.412	0.000	0.162		0.412	0.000
Subjective norms→Behavioral willingness→Behavior	Direct Effect	0.184	0.059	0.074	0.300	0.001	0.068		0.296	0.002
	Indirect Effect	0.089	0.028	0.044	0.152	0.000	0.042		0.150	0.000
	Total Effect	0.273	0.058	0.160	0.387	0.000	0.160		0.387	0.000
Perceptual behavior control→Behavioral willingness→Behavior	Direct Effect	0.171	0.062	0.044	0.288	0.008	0.046		0.289	0.007
	Indirect Effect	0.081	0.024	0.043	0.139	0.000	0.037		0.132	0.000
	Total Effect	0.252	0.064	0.122	0.375	0.000	0.122		0.374	0.000

Moderating effects. Cotton farmers' willingness to adopt technology influences behaviour, and the degree of influence is moderated by satisfaction with policy and satisfaction with technology services and outcomes. The interaction between adoption intentions and satisfaction with policy and technical services and effects had a significant effect on adoption behaviour, and the path coefficient was positive, indicating that policy satisfaction and satisfaction with technical services and effects positively moderated the effect of adoption intentions on adoption behaviour (Table 3).

Table 3

Results of analysis of moderation effects. ***p < 0.001.
	Behavior		Behavior
Behavioral willingness	0.426	9.692	0.454	11.046
Technology satisfaction	0.233	5.31	0.223	5.451
Interaction item 1			0.314	7.807
R2	0.275		0.373
F	74.209***		77.374***
Behavioral willingness	0.446	10.161	0.47	10.947
Policy satisfaction	0.197	4.495	0.191	4.477
Interaction item 2			0.206	4.849
R2	0.261		0.303
F	69.062***		56.529***

Behavioural Beliefs Dimension. According to the 'economic man' hypothesis, the ability of a new technology to secure or enhance economic benefits is a major consideration in the adoption of SA technology. Although returns are highly attractive, the impact of the technology on returns is much smaller than the impact of the price of cotton. Secondly, the effect on increasing yields and reducing farming costs is less obvious, as yields are mainly influenced by factors such as weather and seed. Again, the new technology reduces material inputs through accurate measurement, leading to lower production costs. In addition, the reduction in labour plays an integral role in the willingness to adopt SA technologies. As a result of urbanisation, there has been a significant labour exodus from the countryside, leading to an ageing rural population at this stage, and cotton farmers are more interested in adopting new technologies to save time and reduce their workload. The benefits of technology come with risks. Cotton farmers are often reluctant to adopt SA technology and related services if they perceive that the results are not good and that they will not only not benefit from the technology but will also suffer financial losses. In addition, the SA technology itself and the lack of technical staff to follow up and guide the adoption or the failure of machinery to be repaired in time lead to a poor perception of use by cotton farmers, who after a short period of implementation will still be planting in the traditional way as before.

Normative Beliefs Dimension. With the government and agronomy-related departments promoting smart agriculture policies through top-down efforts, some localities have established national agricultural science and technology parks to provide technical guidance to cotton farmers in the field, playing a positive demonstration and driving role. It enables cotton farmers to understand more directly the functions and benefits of SA technology, thus eliminating their concerns about SA technology. The dealers mainly promote agricultural products and do not promote the new technology to a great extent. Secondly, some villages, regimental collectives or new management bodies and other organisations will force cotton farmers to adopt a particular SA technology, and this mode of promotion causes a disconnect between the supply of technology and actual demand. However, this collective technical service effectively alleviates the high cost, high risk and lack of technical management capacity faced by cotton farmers in adopting SA technologies through unified production and technical management. In addition, the government subsidises the purchase of smart farm machinery. However, the subsidies are only for new domestic machinery, while second-hand and imported machinery are not eligible. The connection of social network relations formed by cotton farmers based on geography and kinship makes information transfer and collective communication and decision making the main way of technology diffusion. In rural societies, cotton farmers learn from the effects and experiences of 'opinion leaders', such as local demonstrators, neighbours and co-operative members, who prioritise the adoption of new technologies, and then emulate them to reduce the risks and uncertainties of adopting SA technologies. In addition, the vast majority of cotton farmers make decisions with the input of family members. However, some family members are not involved in growing cotton and cannot provide constructive advice to inform their decisions.

Control beliefs dimension. Most cotton farmers spend their free time learning about the subject or attending technical training to improve their skills. The local authorities also organise visits to exhibitions and exchanges of planting experience. In addition, relying on the development of modern information networks, cotton farmers use their smartphones to get more information about SA with the help of new media channels such as public numbers, academic lectures or the Jitterbug APP, and search for SA technologies that interest them for in-depth understanding. However, there are still some cotton farmers who are affected by factors such as literacy, age, closed production and inability to use smartphone functions proficiently, making it difficult to access the new technologies they need. From the cotton farmers' own risk tolerance. Risk-averse people are mostly large growers or younger, more educated cotton farmers who are willing to give priority to trying new technologies. Large growers who have test plots will select a small portion of land for experimentation. Risk-averse people, on the other hand, are generally small or older cotton farmers who are resistant to adopting new technologies and have a weaker risk tolerance. At this stage, the traditional, conservative, smallholder ideology of refusing to accept new things is still prevalent. In terms of finance, cotton farmers will work off funds through agricultural loans and other means even if they do not have sufficient funds as long as they see the benefits of the technology.

In this paper, 394 micro research data were used to conduct empirical analysis through DTPB, and then to dig deeper into the factors affecting cotton farmers' SA technology adoption behaviour at the micro level. The conclusions are as follows: (1) Although there are risks in using the technology, cotton farmers care more about the change in welfare level brought by the technology itself. Perceived usefulness, in descending order of intensity of willingness, is yield, industrial development, labour, economic returns, water quantity and pesticide and fertiliser use; perceived risk, the higher than average price of technical services for new technologies discourages adoption. (2) The degree of influence of government publicity and subsidies on the willingness to adopt SA technology is greater among the superior influences, followed by villages and groups. Dealers, on the other hand, mainly promote agricultural products and do not promote new technologies to too great an extent; in peer influence, the vast majority of cotton farmers listen to the recommendations of their friends and relatives when making decisions. This is followed by the opinions of family members. (3) Cotton farmers' willingness to adopt SA technology is mainly influenced by technological convenience, and information transfer within village groups is more accessible than that of online information trading platforms. This is followed by self-efficacy. In addition, cotton farmers believe that only the purchase of machinery costs a lot of money, while the expenditure on technical services is generally acceptable and will also use their free time to attend various trainings. (4) Subjective norms and perceived behavioural control can directly promote cotton farmers' willingness and behaviour to adopt smart farming technologies, and can also indirectly influence behaviour through willingness to adopt. (5) Cotton farmers' satisfaction with policies and technologies has a positive moderating effect on the adoption behaviour of smart farming technologies.

Data availability

The raw data and collated data supporting the fndings of this study could be made available from the corresponding author upon judicious request.

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No competing interests reported.

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Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

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