Research on the Improvement of Digital Literacy for Moderately Scaled Tea Farmers under the Background of Digital Intelligence Empowerment

Abstract

In the context of digital intelligence empowerment, the digital literacy level of tea farmers has a significant impact on the intelligent development and transformation of the tea industry. This study extends the original model of the unified theory of acceptance and use of technology (UTAUT) by introducing the personal innovativeness theory and the self-efficacy theory and constructs a new model to explore the influencing factors of moderately scaled tea farmers’ digital literacy improvement behavior. There are a total of 22 research hypotheses. Using structural equation modeling and collecting questionnaire data for analysis, the following research results were obtained. (1) The performance expectancy, social influence, effort expectancy, personal innovativeness, and self-efficacy all significantly positively affected the willingness of tea farmers to improve their digital literacy, according to the path coefficient in descending order: social influence (0.226) > self-efficacy (0.224) > effort expectancy (0.178) > performance expectancy (0.157) > personal innovativeness (0.155). (2) Facilitating conditions and the willingness to improve digital literacy had a significant positive impact on tea farmers’ digital literacy improvement behavior, according to the size of the path coefficient: the willingness to improve (0.271) > facilitating conditions (0.106). (3) The willingness of tea farmers to improve their digital literacy played a complete mediating role between personal innovativeness and self-efficacy on their digital literacy improvement behavior, and was partially mediated between the performance expectancy, social influence, and effort expectancy on their digital literacy improvement behavior. According to the proportion of indirect effects, the order was effort expectancy (27%), performance expectancy (47%), and social influence (49%). (4) The gender and age of tea farmers had a significant positive moderating effect on the impact of performance expectancy on the willingness to improve digital literacy. Age and experience had a significant positive moderating effect on the impact of effort expectancy on the willingness to improve digital literacy. The age of tea farmers had a significant positive moderating effect on the improvement of digital literacy behavior through the facilitating conditions. This study extended the applicability of the UTAUT theoretical model and proposed six strategies to improve the digital literacy of tea farmers, which helps policymakers and industry leaders provide practical guidance for tea farmers to improve their digital literacy and provide reference for research related to farmers’ digital literacy.

1. Introduction

Agricultural modernization is currently the trend of more advanced agricultural development in the world [1]. Many modern digital information technologies are being gradually and deeply integrated with agriculture for development. Empowering agricultural development with digital intelligence not only significantly improves agricultural production efficiency, but also promotes the development of economies of scale [2,3]. Similarly, the tea industry is undergoing a digital transformation. Tea is one of the most popular sugar free beverages in the world and is an important economic crop. The tea industry has a high economic value and is an important source of income for many farmers’ livelihoods. China is the birthplace of tea and has a history of developing the tea industry for over 5000 years [4]. Currently, China’s tea production and tea planting areas are ranked first in the world, playing an important role in the tea industry. The digital and intelligent transformation of China’s tea industry is in line with the trends and requirements of world agricultural modernization development. On the basis of the years of accumulated development experience, the Chinese tea industry has promoted its development towards intelligence, digitization, intensification, automation, refinement, and high efficiency through a more scientific and efficient innovative development model. The Chinese tea model can provide a reference for other developing countries to upgrade and develop their tea industries and improve their economic value. At present, the Chinese tea industry is gradually undergoing an intelligent transformation, combining the Internet, the Internet of Things, Big Data, cloud computing, artificial intelligence, and other technologies in various fields such as tea planting, production, processing, management, and sales. Digital literacy will be an important foundation for intelligent agricultural development. Tea farmers operating on a moderate scale in China have the foundation and conditions for intelligent development of the tea industry [5,6]. Improving the digital literacy of these tea farmers will help them adapt to the intelligent process of tea industry development. This study selected the land scale as the basic judgment indicator to determine the moderate scale management of tea gardens; that is, the tea plantation management area of tea farmers. The land scale range for moderate scale management of tea gardens was 1.3 to 3.4 hectares [7,8]. Meanwhile, based on the existing research and background, this study defined tea farmers’ digital literacy as their own digital knowledge and skills [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27].

For the research on digital literacy, both the definition of the concepts and connotations and the establishment of the research frameworks have been relatively rich [9]. However, from the perspective of research objects, scholars have conducted more research on students, teachers, civil servants, and library staff [10,11,12,13,14,15]. Compared to the other research object groups, the research on the digital literacy of farmers is relatively small and started relatively late [16,17,18,19,20,21,22,23,24]. Moreover, research on the digital literacy of tea farmers is currently scarce. From the perspective of the research methods, most studies focused on evaluation and adopted qualitative research methods, with fewer empirical analysis methods used for the research on digital literacy. From a research perspective, most of the current research on smart agriculture put forward requirements for farmers’ digital literacy, but research on improving their digital literacy is still rare. Overall, the definition of digital literacy in the field of tea farmers’ behavior is still relatively vague.

In summary, although the existing literature has conducted research on the digital literacy of many subjects, research is rarely based on mature and systematic theoretical frameworks. The overall theoretical frameworks and explanatory power of the existing research are weak, and the persuasiveness of the influencing factors revealed in the research needs to be further improved. At present, the model of unified theory of acceptance and use of technology (UTAUT) is widely used in the field of digital information technology to conduct research on acceptance, willingness, and behavior [28,29]. Although some of the research objects are farmers, most of the research focuses on major food crops or animal husbandry, and little attention is paid to tea farmers or tea as a cash crop [30,31,32,33,34,35,36]. Therefore, in the context of the development of digital literacy empowerment in the tea industry, this article will study the influencing factors of tea farmers’ digital literacy improvement behavior, clarify the impact path and degree, and propose digital literacy improvement strategies based on the data analysis results. This study innovatively used tea farmers as the research object and conducted empirical research into the improvement of tea farmers’ digital literacy. At the same time, this article innovatively expanded the original model of the UTAUT by introducing the personal innovation theory and the self-efficacy theory to study the influencing factors of moderately scaled tea farmers’ digital literacy improvement behavior. Furthermore, through construction and validation, a theoretical model on the influencing factors of tea farmers’ digital literacy improvement behavior was innovatively generated. The value of this study lies in three aspects. Firstly, it enriches the literature research of tea farmers in the field of digital literacy and provides theoretical reference for other similar research issues. Secondly, it expands the applicability of the UTAUT theoretical model in empirical research and its explanatory power on behavior in different research fields. Thirdly, feasible guidance strategies have been proposed, which can provide reference for other developing countries to enhance the digital literacy of tea farmers in the digital empowerment transformation of the tea industry.

2. Theoretical Foundation

2.1. UTAUT Theory

The unified theory of acceptance and use of technology (UTAUT theory) was put forward by Venkatesh and other scholars in 2003 on the basis of integrating eight theories, namely, the “technology acceptance model, theory of reasoned action, theory of planned behavior, motivation model, composite model, PC utilization model, social cognitive theory, and diffusion of innovations” [37]. This theory made up for the defects of the eight theoretical research elements, low prediction and interpretation, and single research perspective, and built the UTAUT theoretical model. Its explanatory power is as high as 70% [38,39]. The UTAUT theoretical model includes four core independent variables, namely the performance expectancy, effort expectancy, social influence, and facilitating conditions. Performance expectancy refers to the degree to which individuals subjectively believe that using information technology will provide assistance and benefits for their work. Effort expectancy refers to the level of effort and perceived difficulty that individuals need to put into the process of adopting and accepting information technology. Social influence refers to the degree to which individuals are influenced by the opinions of others from the outside world when adopting or accepting new information technology, especially the influence of close groups, familiar people, and industry experts around them. Facilitating conditions refers to the level of support provided by an individual’s organizational or technological infrastructure in the process of using information technology. In this model, the behavioral willingness of the study subjects is influenced by three variables: performance expectancy, effort expectancy, and social influence. Facilitating conditions and willingness directly affect individual behavior. At the same time, the model employs four moderating variables, namely gender, age, experience, and voluntariness of the study subjects.

Venkatesh applied the UTAUT theoretical model to organizations with a strong heterogeneity, proving that the explanatory power of the model reached 70%. It has been widely applied by many scholars in fields, such as behavior, psychology, and digital information technology, and has become an effective tool for studying the willingness and behavior of digital information technology acceptance. It is a relatively authoritative and typical model [30,40,41,42]. During the application and research process of the UTAUT theoretical model, it is allowed to increase or decrease the variables in the model according to the characteristics and research needs of the different research objects. Additionally, it is able to integrate other research theories to further improve the explanatory power of the model. At present, the UTAUT theoretical model is widely used, and scholars have used the UTAUT theoretical model as the basis for the construction of research models in related studies, such as the willingness and acceptance behavior of farmers’ digital information technology systems, and the acceptance behavior of new agricultural technologies.

In this study, tea farmers’ digital literacy mainly referred to their own digital knowledge and skills. The improvement of tea farmers’ digital literacy was manifested in the learning and acceptance of digital knowledge and skills, and the strengthening of their own digital knowledge and skills, which was the tea farmers’ acceptance of digital information technology. Therefore, this study was based on the UTAUT theoretical model to construct a model of influencing factors on the digital literacy improvement behavior of moderately scaled tea farmers (Figure 1).

2.2. Personal Innovativeness Theory

The theory of personal innovativeness was proposed by Midgley and Dowling in 1978, which defines personal innovativeness as the degree of acceptance to new ideas or things by individuals, reflecting their enthusiasm for accepting new things. It is the starting point and driving force for individual innovation activities [43]. Rogers defined personal innovativeness as the tendency of individuals to try new things or accept new technologies in the diffusion of innovations [44]. The stronger the individual’s innovation, the more willing they will be to try and accept new things or technologies and explore the beneficial aspects of new things or technologies for themselves. Scholars such as Agarwal have found in their empirical research on the willingness and behavior to accept information technology that personal innovativeness has a direct or indirect impact on the willingness and behavior to adopt new information technology [45]. The stronger an individual’s innovation, the stronger their willingness or behavior to accept new information technology. Personal innovativeness is an inherent characteristic of individuals, and individuals with different levels of innovation often have different cognitive and processing methods towards new things. Individuals with stronger innovation are more willing to actively try new things, accept new knowledge or technology, and then engage in innovative practices, while individuals with lower innovation are the opposite. Currently, many scholars have found that personal innovativeness has a certain impact on the users’ willingness and acceptance behavior for information technology adoption. Personal innovativeness was, therefore, considered in research.

2.3. Self-Efficacy Theory

The theory of self-efficacy was proposed by American psychologist Bandura in 1977 [46]. It proposes that self-efficacy is an individual’s subjective judgment of their ability to complete a task and achieve expected goals in a certain context. It can also be understood as an individual’s level of confidence for organizing and executing a series of actions to produce certain results, which to some extent affects the success of a task. For different research fields or research objects, self-efficacy will show differences and have a certain degree of specificity. In specific studies, self-efficacy can better predict individual willingness and behavior. Studies have shown that self-efficacy can have varying degrees of impact on an individual’s cognition, emotions, motivation, and behavior, sometimes mediated by cognition, emotions, and motivation [47]. Self-efficacy is a complex process of self-persuasion. The stronger a person’s self-efficacy, not only can they have a positive view of things but they can also develop a self-reinforcement within themselves and externalize it into actual human behavior. Therefore, they voluntarily adhere to and practice self-efficacy, leading to a greater possibility of achieving success, while a weaker individual’s self-efficacy is the opposite. The UTAUT theory was formed on the basis of eight major theories, including Bandura’s social cognition theory, and the self-efficacy theory is one of the constituent theories of the social cognition theory. Venkatesh did not consider self-efficacy variables when constructing the UTAUT theory model, but self-efficacy can have a certain impact on individual behavior and willingness, which can show differences according to different studies.

Therefore, this study extended the UTAUT original model by integrating the personal innovativeness theory and the self-efficacy theory, taking into account the two influencing factors of personal innovativeness and self-efficacy, and exploring the extent to which the personal innovativeness and self-efficacy of tea farmers affected their willingness and behavior to improve their digital literacy.

3. Hypotheses Development

3.1. Performance Expectancy and Willingness for Improving Digital Literacy

In this study, performance expectancy refers to the degree to which tea farmers subjectively believe that the digital knowledge and skills they possess after improving their digital literacy can provide assistance and benefits for the intelligent development of their tea planting, production, and daily management of tea gardens. Venkatesh’s research indicated that the performance brought about by information technology or information systems that individuals receive positively affects their willingness to accept those technologies [37]. In the field of digital information technology acceptance, willingness, and behavior, the performance expectancy in the UTAUT theoretical model is considered the most powerful predictive tool. If tea farmers improve their digital literacy, they can more easily access relevant information about tea; apply relevant digital equipment and platforms in tea cultivation, production, processing, and sales; and improve work efficiency and create profits. They will be willing to improve their digital literacy [48]. Based on this, the following hypothesis was proposed.

H1. 

Performance expectancy has a significant positive impact on tea farmers’ willingness to improve their digital literacy.

3.2. Social Influence and Willingness for Enhancing Digital Literacy

Social influence refers to the degree to which tea farmers are influenced by the opinions of others around them, such as family and friends, industry experts, and government calls, when they adopt digital knowledge and skills to improve their digital literacy. Venkatesh believed that an individual’s willingness and behavior were influenced by important stakeholders, and when social influence was positive, it could positively enhance the individual’s willingness and behavior [37]. Lima found that farmers were more willing to adopt new agricultural related technologies promoted by the government [49]. Based on this, the following hypothesis was proposed.

H2. 

Social influence has a significant positive impact on tea farmers’ willingness to improve their digital literacy.

3.3. Effort Expectancy and Willingness for Improving Digital Literacy

Effort expectancy refers to the level of effort and perceived difficulty that tea farmers need to put into learning digital knowledge and mastering digital skills in the process of improving their digital literacy. Venkatesh believed that individuals would weigh the effort and difficulty required before learning and using new technologies, and the effort expectancy would have a positive impact on their willingness to use new technologies [37]. Davis believed that individual acceptance and the use of any information technology would require time and effort in order to understand, master, and apply it [50]. If the cost of the time and effort spent on learning and using information technology is too high, then individuals will reduce their willingness to accept it. If tea farmers are willing to invest time and effort in improving their digital literacy and believe that the difficulty level is not high, then tea farmers will be more willing to improve their digital literacy. Based on this, the following hypothesis was proposed.

H3. 

Effort expectancy has a significant positive impact on the willingness of tea farmers to improve their digital literacy.

3.4. Personal Innovativeness and Willingness for Enhancing Digital Literacy

Personal innovativeness refers to the acceptance and enhancement of digital knowledge and skills by tea farmers through innovative attempts to develop the tea industry using smart agricultural technology, leading to the improvement of their digital literacy. Van Raaij and Tom have shown that individuals with stronger innovation are more inclined to embrace and use new technologies [51,52]. Rogers believed that individuals with a high level of innovation awareness were more willing to accept and use new technologies, and were able to bear the risks or uncertainties associated with the use of new technologies [44]. Obienu and Amadin’s research showed that if individuals possess high levels of personal innovativeness, they were more likely to master new technologies and apply them more quickly [53]. In the context of digital intelligence empowerment, modern digital information technology and agriculture are gradually being deeply integrated and developed. Tea farmers developing smart agriculture need to have the digital knowledge and skills that match it. Tea farmers have a stronger willingness to innovate and apply digital technology in activities such as tea planting, production, and tea garden management, and will be more willing to improve their digital literacy. Based on this, the following hypothesis was proposed.

H4. 

Personal innovativeness has a significant positive impact on the willingness of tea farmers to improve their digital literacy.

3.5. Self-Efficacy and Willingness for Improving Digital Literacy

Self-efficacy refers to the subjective judgment of tea farmers on whether they can improve their digital knowledge and skills and achieve their inner expectancy when improving their digital literacy in the context of the digital age; that is, the level of confidence in improving their digital literacy and achieving certain results. Bandura and Cervone believed that self-efficacy emphasized an individual’s subjective judgment and was their subjective expectancy of being competent for a certain job [47]. Tsai H S and Kundu believed that an individual’s self-efficacy towards new technologies was reflected in their belief toward learning knowledge and mastering new skills related to new technologies, and that their self-efficacy would have an impact on their behavioral intentions [54,55]. If tea farmers have a high sense of self-efficacy, they will have a positive view of improving their digital literacy and will be more proactive in adhering to the improvement of their digital literacy. They will confidently overcome the difficulties in the improvement process and be confident that they can achieve their goals. Based on this, the following hypothesis was proposed.

H5. 

Self-efficacy has a significant positive impact on tea farmers’ willingness to improve their digital literacy.

3.6. Facilitating Conditions and Digital Literacy Improvement Behavior

Facilitating conditions refers to the degree to which tea farmers can obtain support from relevant organizations or external resources during the process of improving their digital literacy. They can be specifically defined as resources that can be allocated to enhance their digital literacy and acquire the relevant digital knowledge and skills, such as economic conditions, land conditions, convenient ways to obtain new agricultural technologies, methods to solve difficulties, and policies and subsidies provided by the government. Venkatesh believed that if individuals were able to receive new technologies or things with favorable external conditions [37], they would be more willing to learn and accept them. If tea farmers can receive help from the government or other organizations, as well as support from external resources, during the process of improving their digital literacy, it will help them improve their digital knowledge and skills. Based on this, the following hypothesis was proposed.

H6. 

Facilitating conditions have a significant positive impact on tea farmers’ digital literacy improvement behavior.

3.7. Willingness and Behavior for Enhancing Digital Literacy

The willingness of tea farmers to improve their digital literacy refers to the degree to which they are willing to try to improve their digital knowledge and skills. The behavior of improving digital literacy mainly refers to the recent improvement of digital knowledge and skills by tea farmers, as well as the subsequent strengthening of their own digital literacy. Angel, Ahikiriza, and other scholars confirmed that behavioral intention had a significant positive effect on behavior [56,57]. Zhang weiwei, Turner, and other scholars believed that behavioral intention could predict actual behavior [58,59]. Most theoretical models on technology adoption and acceptance include two variables, willingness and behavior, which can predict and judge behavior. Based on this, the following hypothesis was proposed.

H7. 

The willingness of tea farmers to improve their digital literacy has a significant positive impact on their digital literacy improvement behavior.

3.8. The Mediating Role of Willingness for Enhanceing Digital Literacy

In the original model of the UTAUT theory, performance expectancy, social influence, and effort expectancy have a positive impact on willingness, which then affects behavior. From the causal logic of the UTAUT theory model, there is an inherent connection between performance expectancy, social influence, and effort expectancy and willingness and behavior. Moreover, the UTAUT theory model exerts different degrees of influence on willingness and behavior through the influencing factors of the model under different backgrounds and research object conditions. The models have varying degrees of explanatory power for behavior prediction [60]. Therefore, this study assumed that the willingness of tea farmers to enhance their digital literacy had a mediating effect on performance expectancy, social influence, effort expectancy, personal innovativeness, and self-efficacy for promoting their digital literacy. Based on this, the following hypotheses were proposed.

H8. 

The willingness of tea farmers to improve their digital literacy plays a mediating role between performance expectancy and tea farmers’ digital literacy improvement behavior.

H9. 

The willingness of tea farmers to enhance their digital literacy plays a mediating role between social influence and tea farmers’ digital literacy improvement behavior.

H10. 

The willingness of tea farmers to enhance their digital literacy plays a mediating role between effort expectancy and tea farmers’ digital literacy improvement behavior.

H11. 

The willingness of tea farmers to enhance their digital literacy plays a mediating role between personal innovativeness and tea farmers’ digital literacy improvement behavior.

H12. 

The willingness of tea farmers to enhance their digital literacy plays a mediating role between self-efficacy and tea farmers’ digital literacy improvement behavior.

3.9. The Moderating Effect of Gender, Age, and Experience

According to the characteristics of the tea farmers and research needs, the regulatory variables in the original model of UTAUT theory were adjusted and the “voluntariness” regulatory variables were deleted. Since the willingness and behavior of tea farmers to improve their digital literacy in this study were initiated independently, the tea farmers could choose whether to improve their digital literacy according to their own preferences [61]. Therefore, the “voluntariness” moderating variable could not be used as a reference in this study model. Firstly this study did not retain the moderating variable of “voluntariness”. Secondly, the regulating variable “experience” in the original model of the UTAUT theory was concretized as “time spent on tea cultivation”. Based on this, the following hypotheses were proposed.

H13. 

Gender plays a moderating role on the impact of performance expectancy on tea farmers’ willingness to improve their digital literacy.

H14. 

Gender plays a moderating role on the social influence on tea farmers’ willingness to improve their digital literacy.

H15. 

Gender plays a moderating role on the impact of effort expectancy on tea farmers’ willingness to improve their digital literacy.

H16. 

Age plays a moderating role on the impact of performance expectancy on tea farmers’ willingness to improve their digital literacy.

H17. 

Age plays a moderating role on the social influence on tea farmers’ willingness to improve their digital literacy.

H18. 

Age plays a moderating role on the impact of effort expectancy on tea farmers’ willingness to improve their digital literacy.

H19. 

Age plays a moderating role on the influence of facilitating conditions on tea farmers’ digital literacy improvement behavior.

H20. 

Experience plays a moderating role on the social influence on tea farmers’ willingness to improve their digital literacy.

H21. 

Experience has a moderating effect on the willingness of tea farmers to improve their digital literacy through effort expectancy.

H22. 

Experience has a moderating effect on the improvement of tea farmers’ digital literacy behavior through facilitating conditions.

4. Research Methodology

4.1. Research Model

Based on the above theoretical foundations and research hypotheses, this article extended the UTAUT theoretical model and innovatively established a new research model to explore the digital literacy improvement behavior of tea farmers in moderately scaled operations. The model included independent variables, intermediary variables, moderating variables, and dependent variables (Figure 2).

4.2. Questionnaire Design

The production of this research questionnaire was based on the existing relevant literature, which was modified and improved under the guidance of professors from the Department of Tea Science and the Department of Business Economics of Anxi Tea College, Fujian Agriculture and Forestry University [37,43,44,46,49,50,57,60,61]. After completing the initial questionnaire production, a pilot study was conducted on the questionnaire of tea farmers in Anxi in August 2022 to enhance the scientificity and accuracy of the questionnaire items. A total of 112 survey questionnaires were distributed for the pilot study of the questionnaire, and 102 valid questionnaires were collected, with a questionnaire effectiveness rate of 91.1%. By applying the SPSS software to conduct reliability tests on the data of the variables in the questionnaire, it was found that Cronbach’s α of each variable met the standard of greater than 0.7, indicating that the questionnaire had a certain degree of reliability. Secondly, validity testing was conducted on the questionnaire, and it was found that the KMO values of each variable reached the standard of higher than 0.5. The Bartlett sphericity test showed a significance of less than 0.001, factor loading greater than 0.5, square difference AVE values greater than 0.5, and CR values greater than 0.7, indicating that the questionnaire passed the validity test. However, it was also found that some tea farmers did not have a clear understanding of the measurement items for some variables. Therefore, based on the feedback, appropriate adjustments were made, and a scientifically reasonable questionnaire was ultimately formed. The survey questionnaire consisted of three parts. The first part was a questionnaire explanation, which mainly explained the purpose of the questionnaire and explained the related concepts, so that the respondents could understand the background of the questionnaire and effectively answer the questionnaire content. The second part was about the basic situation investigation of the tea farmers. The third part was the related items of the eight variables in the constructed model. These eight variables were, respectively, the tea farmers’ performance expectancy, social influence, effort expectancy, personal innovativeness, self-efficacy, facilitating conditions, digital literacy promotion willingness, and digital literacy promotion behavior. Each variable was represented by four to six items. These items were scored and measured using the Likert five point scale (1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree).

4.3. Data Collection and Analysis Method

From September to November 2022, the questionnaire responses were collected in Anxi County, Fujian Province, a famous tea producing area in China. The survey questionnaire was distributed in 11 relatively contiguous tea areas in Anxi County, including Gande Town, Xiping Town, Lutian Town, Huqiu Town, Jiandou Town, Penglai Town, Changkeng Township, Longjuan Township, Xianghua Township, Lantian Township, and Daping Township. The data and information obtained in this study were collected through face-to-face offline questionnaire surveys and interviews among tea farmers with relatively continuous planting areas, ranging from 1.3 to 3.4 hectares. A total of 464 questionnaires were collected, with 440 valid questionnaires. The recovery rate for the valid questionnaires was approx. 95%.

In this study, the SPSS and AMOS software were used to sort and analyze the collected data. The analysis content mainly included a descriptive statistical analysis of the sample’s basic characteristics, reliability and validity tests of the sample data, structural equation model verification, and a mesomeric effect and moderation test.

4.4. Demographic Profile

As shown in

Table 1. Demographic characteristics.

Demographic	Category	Frequency	Proportion
Gender	Male	230	52.30%
	Female	210	47.70%
Age	26–35 years old	142	32.30%
	36–45 years old	209	47.50%
	46–55 years old	71	16.10%
	Over 56 years old	18	4.10%
Education level	Primary school	48	10.90%
	Middle school	153	34.80%
	High school and technical secondary school	163	37.00%
	College	63	14.30%
	Bachelor’s degree or above	13	3.00%
Join the tea cooperative	Yes	263	59.80%
	No	177	40.20%
Participated in a farmer training program	Yes	51	11.60%
	No	389	88.40%
Engaged in tea cultivation time	2–5 years old	7	1.60%
	6–10 years old	146	33.20%
	11–15 years old	152	34.50%
	16–20 years old	79	18.00%
	21–25 years old	46	10.50%
	Over 26 years	10	2.30%
Operating Tea Garden Area	[1.33–1.67) hectares	193	43.90%
	[1.67–2) hectares	151	34.30%
	[2–2.33) hectares	64	14.50%
	[2.33–2.66) hectares	21	4.80%
	[2.66–3) hectares	7	1.60%
	[3–3.34) hectares	4	0.90%
Daily usage time of electronic products	(0.2) h	89	20.20%
	[2.4) h	329	74.80%
	Over 4 h	22	5.00%
Understanding of Smart Agriculture	Fully understanding	5	1.10%
	Basic understanding	75	17.00%
	General understanding	213	48.40%
	Don’t understand much	146	33.20%
	Lack of understanding	1	0.20%

, in terms of gender, there was not much difference between the proportion of males and females. In terms of age, the proportion of people aged 26 to 45 was relatively large. In terms of educational level, the majority of tea farmers mainly had junior high school, high school, and technical secondary school degrees, with a relatively small proportion of people at or above university level. Moreover, most tea farmers had not participated in farmer training. The proportion of tea farmers engaged in tea cultivation for 6–15 years was as high as 67.7%. In terms of an understanding of smart agriculture, the proportion of people who fully understood it was 1.1%, while the proportion of people who basically understood it was 17%. The data indicated that a majority of tea farmers had limited exposure to smart agriculture. With a general understanding of 48.4% and a lack of understanding of 33.2%, it indicated that most tea farmers were in a state of partial understanding regarding smart agriculture.

5. Results of Statistical Analysis

5.1. Measurement Model Analysis

This study applied SPSS software to analyze the reliability and effectiveness of the eight measurement dimensions in the questionnaire [62].

Table 2. Loadings and composite reliability.

Variable	Item	Loading	Cronbach’s α	AVE	CR
Performance expectancy	PE1	0.765	0.901	0.604	0.901
	PE2	0.762
	PE3	0.772
	PE4	0.781
	PE5	0.774
	PE6	0.808
Social influence	SI1	0.698	0.874	0.586	0.876
	SI2	0.698
	SI3	0.851
	SI4	0.745
	SI5	0.823
Effort expectancy	EE1	0.691	0.875	0.586	0.876
	EE2	0.788
	EE3	0.818
	EE4	0.764
	EE5	0.762
Personal innovativeness	PI1	0.717	0.890	0.578	0.891
	PI2	0.776
	PI3	0.772
	PI4	0.746
	PI5	0.766
	PI6	0.781
Self-efficacy	SE1	0.859	0.867	0.626	0.869
	SE2	0.842
	SE3	0.71
	SE4	0.743
Facilitating conditions	FC1	0.738	0.871	0.577	0.872
	FC2	0.804
	FC3	0.728
	FC4	0.761
	FC5	0.766
Willingness	W1	0.786	0.833	0.655	0.851
	W2	0.829
	W3	0.812
Behavior	B1	0.751	0.800	0.613	0.826
	B2	0.772
	B3	0.824

shows Cronbach’s α for the eight measurement dimensions. The Cronbach’s α coefficients were all greater than 0.8. The coefficient test results all met the qualified standards, indicating that the measurement model had a high reliability and a good reliability. The convergence validity reflected the degree of correlation between different items of the same variable. The evaluation criteria for determining whether the convergence validity of a model met the standard could be determined by observing the item factor load, AVE value (average variance extracted), and CR value (composite reliability). The factor load of the general items should have been greater than 0.5, the AVE value of the extracted square difference should have been greater than 0.5, and the CR value should have been greater than 0.7. The convergent validity of the questionnaire was judged by these three criteria [63,64,65,66]. The factor load of each item corresponding to the eight variables was above 0.6, meeting the standard requirement of a factor load greater than 0.5 and indicating that the items in the scale had a good representativeness for the variables to be measured. The extracted difference of the two squares AVE value of the eight variables was greater than 0.5 and the combined reliability CR value was greater than 0.8. The CR value met the standard requirement of greater than 0.7, which indicated that the measurement model had a good internal consistency and convergence validity.

Divergent validity refers to the degree of correlation or significant difference between the measurement variables of different variables. Divergence validity is generally measured and evaluated by comparing the square roots of the AVE of each variable with its correlation coefficient. After rooting the AVE of each variable, if the square roots of the AVE of each variable are greater than the correlation coefficient between the variable and other variables, it indicates a good divergence validity among the variables in the model [67]. As shown in

Table 3. AVEs and correlation coefficients of constructs.

	FC	SE	PI	EE	SI	PE	W	B
FC	0.577
SE	0.327	0.626
PI	0.326	0.421	0.578
EE	0.491	0.563	0.567	0.586
SI	0.270	0.321	0.296	0.329	0.586
PE	0.300	0.345	0.291	0.334	0.368	0.604
W	0.318	0.512	0.464	0.517	0.462	0.419	0.655
B	0.466	0.511	0.526	0.672	0.451	0.439	0.630	0.613
Square root value of AVE	0.760	0.791	0.760	0.766	0.766	0.777	0.809	0.783

(FC: facilitating conditions; SE: self-efficacy; PI: personal innovativeness; EE: effort expectancy; SI: social influence; PE: performance expectancy; W: willingness; B: behavior).

, the root of each AVE of the eight variables was greater than the correlation coefficient of each paired variable, indicating that there was a good divergence validity between the variables in the measurement model.

5.2. Structural Equation Model Analysis

As shown in

Table 4. Validity factor analysis model fit.

Index Name	CMIN/DF	GFI	RMR	RMSEA	NFI	TLI	CFI	IFI	PNFI
Index criteria	<2	[0.7,0.9)	<0.05	<0.08	[0.7,0.9)	>0.9	[0.7,0.9)	[0.7,0.9)	>0.5
Actual value	1.894	0.883	0.031	0.045	0.884	0.935	0.941	0.942	0.799

, the various indicators of the initial model of influencing factors on the digital literacy improvement behavior of moderately scaled tea farmers were basically on the ideal fit index coefficient standard, with a CMIN/DF of 1.894, a GFI value of 0.883, an RMR value of 0.031, an RMSEA value of 0.045, an NFI value of 0.884, a TLI value of 0.935, a CFI value of 0.941, an IFI value of 0.942, and a PNFI value of 0.799. Therefore, the adaptability of the model in this study was relatively good and theoretically acceptable [68].

Table 5. Hypotheses testing results.

Path			Standardized Path Coefficient	S.E.	C.R.	P
W	←	PE	0.154	0.064	3.022	0.003
W	←	SI	0.230	0.060	4.449	***
W	←	EE	0.177	0.065	2.705	0.007
W	←	PI	0.158	0.065	2.745	0.006
W	←	SE	0.218	0.069	3.658	***
B	←	W	0.263	0.057	4.170	***
B	←	PE	0.100	0.054	2.108	0.035
B	←	SI	0.111	0.051	2.286	0.022
B	←	EE	0.331	0.060	4.941	***
B	←	PI	0.100	0.054	1.871	0.061
B	←	SE	0.041	0.058	0.734	0.463
B	←	FC	0.114	0.049	2.252	0.024

*** p < 0.001.

shows the results of the initial model path analysis report. The performance expectancy, social influence, effort expectancy, personal innovativeness, and self-efficacy all had a significant positive impact on the willingness of tea farmers to improve their digital literacy (p < 0.05), assuming that H1, H2, H3, H4, and H5 were valid. The facilitating conditions and tea farmers’ willingness to improve their digital literacy had a significant positive impact on their digital literacy improvement behavior (p < 0.05), assuming that H6 and H7 were valid. The performance expectancy, social influence, and effort expectancy all had a significant positive impact on the tea farmers’ digital literacy improvement behavior (p < 0.05), while personal innovativeness and self-efficacy did not have a significant positive impact on the tea farmers’ digital literacy improvement behavior (p > 0.05). This indicated that the tea farmers’ willingness to improve digital literacy could play a partial mediating role between the performance expectancy, social influence, and effort expectancy on digital literacy improvement behavior, and a complete mediating role between personal innovativeness and self-efficacy for enhancing digital literacy behavior, assuming H8, H9, H10, H11, and H12 were valid.

Based on the fitting coefficients of the initial model and the path analysis report, the initial model was modified to remove insignificant paths. At the same time, the residual terms with a strong correlation were connected according to the corrected index MI value to obtain a more ideal structural model. As shown in

Table 6. Corrected validity factor analysis model fitting.

Index Name	CMIN/DF	GFI	RMR	RMSEA	NFI	TLI	CFI	IFI	PNFI
Index criteria	<2	[0.7,0.9)	<0.05	<0.08	[0.7,0.9)	>0.9	[0.7,0.9)	[0.7,0.9)	>0.5
Actual value	1.750	0.892	0.031	0.041	0.893	0.946	0.951	0.951	0.804

, compared with the overall adaptation results of the initial model, all the indicators of the modified model’s fitting coefficient improved and basically met the standard. The fitting degree of the model in this study was good.

As shown in

Table 7. Hypothesis test results after model correction.

Path			Standardized Path Coefficient	S.E.	C.R.	P
W	←	PE	0.157	0.064	3.086	0.002
W	←	SI	0.226	0.059	4.396	***
W	←	EE	0.178	0.066	2.706	0.007
W	←	PI	0.155	0.063	2.711	0.007
W	←	SE	0.224	0.069	3.718	***
B	←	W	0.271	0.055	4.907	***
B	←	PE	0.090	0.054	2.074	0.038
B	←	SI	0.114	0.050	2.583	0.010
B	←	EE	0.409	0.054	7.603	***
B	←	FC	0.106	0.050	2.285	0.022

*** p < 0.001.

, the revised path analysis results reached a significant level. Figure 3 shows the revised structural equation model and path coefficients.

5.3. Test of Mesomeric Effect of Variables

From the path analysis results, we can see that the performance expectancy, social influence and effort expectancy had a direct effect on the tea farmers’ willingness to improve their digital literacy and their behavior to improve their digital literacy. Therefore, we tested the mesomeric effect of the tea farmers’ willingness to improve their digital literacy and explore the proportion of the mesomeric effect. This study used the Bootstrap method to test the mesomeric effect. With reference to the Bootstrap mesomeric effect test procedure, considering the 440 sample data of this study, the sampling times were set to 5000, and the bias correction confidence interval was set to 95%. If the upper and lower limits of the 95% bias correction confidence interval did not contain 0, then the Mesomeric effect was significant.

As shown in

Table 8. Mesomeric effect test results.

		Effect	BootSE	BootLLCI	BootULCI	Effect Proportion
PE	Indirect effect	0.191	0.045	0.114	0.288	47%
	Direct effect	0.217	0.076	0.069	0.365	53%
	Total effect	0.408	0.079	0.258	0.567
SI	Indirect effect	0.205	0.039	0.134	0.285	49%
	Direct effect	0.217	0.064	0.093	0.343	51%
	Total effect	0.421	0.042	0.437	0.600
EE	Indirect effect	0.142	0.034	0.085	0.221	27%
	Direct effect	0.377	0.043	0.289	0.459	73%
	Total effect	0.520	0.063	0.297	0.548

, the willingness of tea farmers to improve their digital literacy played a partial mediating role in the impact between the performance expectancy, social influence, effort expectancy, and digital literacy improvement behavior. According to the proportion of indirect effects, the order was effort expectancy (27%), performance expectancy (47%), and social influence (49%).

5.4. Verification of the Moderating Effect of Variables

This study used Model 1 in the SPSS plugin process to test the moderating effects of the three moderating variables of the tea farmers’ gender, age, and experience in the model [69].

As shown in

Table 9. Regulatory effect test results.

Variable	Coeff	se	t	p	R2	F
constant	3.320	0.685	4.847	0.000	0.147	24.937
PE	0.055	0.163	0.335	0.738
Gender	−0.887	0.412	−2.154	0.032
PE × Gender	0.217	0.098	2.219	0.027
constant	2.310	0.561	4.118	0.000	0.171	29.870
SI	0.314	0.146	2.154	0.032
Gender	−0.269	0.359	−0.750	0.454
SI × Gender	0.086	0.094	0.914	0.361
constant	2.800	0.403	6.948	0.000	0.197	35.719
EE	0.232	0.123	1.886	0.060
Gender	−0.312	0.252	−1.242	0.215
EE × Gender	0.105	0.077	1.371	0.171
constant	4.629	0.644	7.184	0.000	0.180	31.981
PE	−0.198	0.156	−1.272	0.204
Age	−0.907	0.207	−4.376	0.000
PE × Age	0.200	0.051	3.946	0.000
constant	2.635	0.626	4.212	0.000	0.180	31.992
SI	0.320	0.167	1.917	0.056
Age	−0.216	0.191	−1.131	0.259
SI × Age	0.034	0.052	0.649	0.517
constant	4.080	0.444	9.198	0.000	0.224	42.026
EE	−0.155	0.138	−1.125	0.261
Age	−0.576	0.139	−4.152	0.000
EE × Age	0.183	0.045	4.067	0.000
constant	3.513	0.560	6.271	0.000	0.238	45.492
FC	0.109	0.148	0.737	0.462
Age	−0.531	0.176	−3.017	0.003
FC × Age	0.097	0.047	2.069	0.039
constant	2.690	0.647	4.156	0.000	0.186	33.112
SI	0.323	0.171	1.887	0.060
Experience	−0.166	0.145	−1.146	0.252
SI × Experience	0.023	0.039	0.581	0.561
constant	4.074	0.464	8.778	0.000	0.221	41.327
EE	−0.115	0.144	−0.798	0.425
Experience	−0.407	0.105	−3.867	0.000
EE × Experience	0.119	0.034	3.528	0.001
constant	2.830	0.589	4.801	0.000	0.235	44.571
FC	0.304	0.155	1.955	0.051
Experience	−0.218	0.134	−1.628	0.104
FC × Experience	0.023	0.036	0.652	0.515

, the standardized coefficient of the interaction term between the tea farmers’ gender and performance expectancy on their willingness to improve digital literacy was 0.217, p < 0.05. The gender of tea farmers played a positive moderating role in the impact of performance expectancy on their willingness to improve digital literacy, assuming H13 was valid. The significant p-values corresponding to the interaction terms of gender, social influence, and effort expectancy of tea farmers were greater than 0.05, assuming that H14 and H15 were not valid. The standardized coefficient of the interaction between the age and performance expectancy of tea farmers on their willingness to improve digital literacy was 0.200, the standardized coefficient of the interaction between age and effort expectancy on their willingness to improve digital literacy was 0.183, and the standardized coefficient of the interaction between age and the facilitating conditions on their willingness to improve digital literacy was 0.097, with significant p-values less than 0.05. This indicated that the age of tea farmers played a positive moderating role in the impact of performance expectancy and effort expectancy on the tea farmers’ willingness to improve digital literacy, and that age played a positive moderating role in the impact of facilitating conditions on tea farmers’ digital literacy improvement behavior, assuming H16, H18, and H19 were valid. The significant p-value corresponding to the interaction between the age and social influence of tea farmers was greater than 0.05, assuming that H17 was not valid. The significant p-value of the interaction term between the experience of tea farmers and social influence was greater than 0.05, assuming that H20 was not valid. The standardized coefficient of the interaction between experience and effort expectancy on the willingness to improve digital literacy was 0.119, with a significant p-value less than 0.05, indicating that the experience of tea farmers played a positive moderating role in the impact of effort expectancy on their willingness to improve digital literacy, assuming H21 was valid. The significant p-value of the interaction term between the experience and facilitating conditions of tea farmers was greater than 0.05, assuming that H22 was not valid. Figure 4 shows the final model of the influencing factors on the digital literacy improvement behavior of moderately scaled tea farmers.

6. Discussion

This study was based on the UTAUT theoretical model, personal innovation theory, and self-efficacy theory to construct a influencing factor model for tea farmers’ digital literacy improvement behavior. Based on the analysis results obtained after verifying the model, the following four mechanisms are discussed.

6.1. Mechanism Discussion on the Direct Influencing Factors of Tea Farmers’ Willingness to Improve Digital Literacy

Performance expectancy, social influence, effort expectancy, personal innovativeness, and self-efficacy all significantly positively affected the willingness of tea farmers to improve their digital literacy. Social influence had the greatest impact on the willingness to improve digital literacy, with a path coefficient of 0.226, followed by self-efficacy, effort expectancy, performance expectancy, and personal innovativeness, with path coefficients of 0.224, 0.178, 0.157, and 0.155, respectively. This indicated that the willingness of tea farmers to enhance their digital literacy increased with an increase in social influence, self-efficacy, effort expectancy, performance expectancy, and personal innovativeness. At the same time, the following five points were analyzed. Firstly, if the government vigorously promotes and popularizes the development concept of the smart tea industry, it can empower tea farmers to enhance their understanding of the development of the tea industry through digital intelligence and provide an opportunity to implement smart tea industry equipment and technology. At the same time, if tea industry experts provide support and affirmation for tea farmers to improve their digital literacy and their family and friends support or encourage them to improve their digital literacy, it will enhance their willingness to improve their digital literacy. Secondly, tea farmers have a high sense of self-efficacy and strong confidence. They do not feel anxious about the acceptance of new knowledge and technology and will not stop due to difficulties in improving their digital literacy. Instead, they will become more courageous and enjoy it [70,71]. Thirdly, if tea farmers receive training in digital literacy and they can find ways to improve their digital literacy and perceive that mastering digital knowledge and skills is not too difficult, they will strengthen their own efforts and expectancy, thereby enhancing their willingness to improve their digital literacy. Fourthly, when tea farmers learn about improving their digital literacy and are able to better engage in tea related work, apply the smart agriculture model for intelligent management of tea gardens, improve tea production efficiency and quality, and use digital platforms to sell tea to increase income and obtain valuable information, it will enhance their willingness to improve their digital literacy [72,73]. Fifthly, tea farmers with strong individual innovation often have a strong interest in agricultural digitization and intelligent technology, a strong ability to actively learn emerging agricultural technologies and tend to try to apply them. The willingness of tea farmers to improve their digital literacy can be enhanced by the enhancement of individual innovation [74].

6.2. Mechanism Discussion on the Factors Influencing Tea Farmers’ Digital Literacy Improvement Behavior

Facilitating conditions significantly positively affect the improvement behavior of tea farmers’ digital literacy. If tea farmers have certain economic capabilities and if the government provides policy support or training subsidies for the improvement of tea farmers’ digital literacy, participating in digital literacy training can effectively improve their digital knowledge and skills. They can seek help from others when encountering difficulties in mastering digital knowledge and skills, which can promote the improvement behavior of tea farmers’ digital literacy [75].

The willingness of tea farmers to improve their digital literacy has a significant positive impact on their digital literacy improvement behavior, indicating that when tea farmers learn about the benefits of improving their digital literacy, they will be willing to improve their digital literacy and take practical actions. At the same time, it will promote the benefits of improving digital literacy among people around them and affect their attention to the improvement of digital literacy [76,77].

6.3. Discussion on the Mechanism of the Indirect Factors Influencing Tea Farmers’ Willingness to Improve Digital Literacy

Firstly, the willingness of tea farmers to enhance their digital literacy can mediate the effects of performance expectancy, social influence, effort expectancy, personal innovativeness, and self-efficacy on their digital literacy improvement behavior, indicating that tea farmers will think rationally about their own digital literacy improvement [78]. The performance expectancy, social influence, and effort expectancy of tea farmers can have a direct impact on their digital literacy improvement behavior, indicating that when the performance expectancy, social influence, and effort expectancy of tea farmers are strong enough, it can promote digital literacy improvement behavior. For example, when tea farmers discover that improving their digital literacy can greatly improve the efficiency and benefits of tea planting, production, and processing, and when the government, experts, family and friends express their support and affirmation at the same time when they perceive that it is not difficult to improve their digital literacy, they will take practical actions to improve their digital literacy [79,80].

In this study, the impact of the personal innovativeness and self-efficacy of tea farmers on digital literacy improvement behavior was not significant. There may have been two possible reasons. Firstly, based on the UTAUT original model, personal innovativeness and self-efficacy, as newly introduced variables, had an uncertain direct impact on behavior after the integration with the UTAUT original model. Through the empirical analysis, it was found that these two new variables affected the willingness to improve digital literacy, and thus affected the improvement behavior. The second was based on the subjective reflection of personal innovativeness and self-efficacy from the perspective of both the variables and tea farmers themselves. When tea farmers were unsure whether new digital knowledge and skills or the difficulty of mastering them would benefit them, or whether there were references around them, they would not take immediate action to improve digital literacy [81].

6.4. Discussion on the Regulatory Mechanisms of Gender, Age, and Experience

Gender and age had a significant positive moderating effect on the impact of performance expectancy on the willingness to improve digital literacy, indicating that males and older tea farmers tended to consider their willingness to improve digital literacy more and focus on the practical value that improving digital literacy could bring to themselves.

Gender, age, and experience did not have a significant moderating effect on the social influence on the willingness to improve digital literacy, indicating that tea farmers currently hold a more traditional development philosophy towards the tea industry. Tea farmers of different genders, ages, and experiences were more cautious when facing new agricultural technologies or digital knowledge and skills. Tea farmers will pay more attention to government support, refer to the opinions and attitudes of tea industry experts and the opinions of family and friends, or consider whether they currently use tea farmers as a reference to make a comprehensive decision on whether to improve digital literacy.

Age and experience had a significant positive moderating effect on the impact of effort expectancy on the willingness to improve digital literacy, while gender had no significant moderating effect on the impact of effort expectancy on the willingness to improve digital literacy in tea farmers. Tea farmers who were older and had been engaged in tea cultivation and production for a longer time accumulated a rich experience in various aspects of tea cultivation, production, and management for many years. In the future, if they are empowered and can develop various aspects with digital knowledge and skills that match the requirements, it will be a significant change to the traditional development mode in the past. Tea farmers can make considerations based on the actual development situation and their own situation, the targeted selection of matching modern digital information technology for empowering development, and the efforts to participate in relevant training to improve one’s digital literacy level in order to better adapt to future development needs. Tea farmers of different genders did not have significant differences in their educational backgrounds, growth environments, or traditional experiences accumulated in tea cultivation, production, and management. Improving their digital literacy requires training and corresponding efforts.

Age had a positive and significant moderating effect on the influence of facilitating conditions on digital literacy improvement behavior, indicating that older tea farmers hoped to receive more support in digital literacy improvement. Learning digital knowledge and skills requires time to adapt and corresponding assistance, not only considering government policy support and training cost subsidies, but also considering the effectiveness of the training [82]. Experience did not have a moderating effect on the influence of facilitating conditions on the improvement of digital literacy behavior, indicating that most tea farmers, regardless of the length of time they had been engaged in tea cultivation, accumulated more traditional experience. There was not much difference between tea farmers. The development of the digital intelligence empowering tea industry is relatively novel for tea farmers, and the facilitating conditions required for them to improve their digital literacy will not differ greatly due to different experiences.

7. Conclusions

This article used the moderately scaled operations of tea farmers as the research object to study the improvement of the digital literacy of tea farmers. By systematically reviewing the relevant research literature and using the “UTAUT theory, individual innovation theory, and self-efficacy theory” as the theoretical basis of this study, the original model of the UTAUT theory was expanded, and a model of the influencing factors on tea farmers’ digital literacy improvement behavior was constructed. Then, the empirical analysis, validation, and improvement were conducted on the model. Finally, the research hypotheses H1, H2, H3, H4, H5, H6, H7, H8, H9, H10, H11, H12, H13, H16, H18, H19, and H21 were validated, and a model of the influencing factors on tea farmers’ digital literacy improvement behavior was obtained. This not only expanded the applicability of the UTAUT theoretical model in empirical research, but also expanded its explanatory power regarding behavior or intention in different research fields. At the same time, based on the empirical analysis results, six feasible guiding strategies for improving the digital literacy of tea farmers were proposed from the perspective of policymakers and industry leaders, promoting tea farmers to better adapt to the digital and intelligent development of the tea industry in the future. Related studies have shown that intelligent agricultural technology is increasingly being used by farmers around the world, especially in many developing countries in Asia. At the same time, the requirements for the digital literacy of farmers for applying technology are also increasing. Therefore, the countermeasures proposed in this study can provide reference for agricultural departments or governments in developing countries [83,84].

7.1. Transform the Development Concept of Tea Farmers and Strengthen the Social Influence of Digital Intelligence Empowering the Development of the Tea Industry

For moderately sized tea farmers, efforts can be made to promote the development model of smart agriculture, guide tea farmers to change their development concepts, shift towards digital and intelligent development, stimulate tea farmers’ enthusiasm and initiative in the development of the “digital intelligence empowerment” tea industry, encourage tea farmers to attach importance to and improve their digital literacy, and enjoy the dividends of intelligent development of the tea industry in the future by becoming beneficiaries of digital intelligence empowerment. For the government, it is possible to collaborate with tea industry research experts to promote practical smart agriculture technologies or tools to tea farmers operating on a moderate scale based on the practical application of smart agriculture technology in the tea industry. This can reduce the burden on the tea industry’s planting and production process, reduce planting production and management costs, improve the quality of tea, increase the benefits, promote tea farmers to improve their digital knowledge and skills, and organize the demonstration role of tea planting technology in large households, allowing them to share their experiences and achievements in digital intelligence empowerment development in a visual way and exchange digital knowledge and skills. Utilizing the advantages of the Internet and new media to build a digital technology exchanging and sharing platform for the tea industry not only expands the promotion of digital and intelligent agricultural technologies, but also creates a good atmosphere for tea farmers to enhance their digital literacy and promote the improvement of their digital literacy.

7.2. Implement Differentiated and Tiered Training Strategies to Enhance Tea Farmers’ Self-Efficacy

Differentiation strategies based on different individual characteristics of tea farmers should be implemented, and tea farmers should be classified and trained based on their different levels of digital literacy, otherwise it could result in a lower self-efficacy due to an inability to adapt to the training content. By adopting a tiered training strategy, starting with simple digital knowledge and skills and then moving from easy to difficult, tea farmers can gradually learn and master new skills from shallow to deep. During the training process, they can gain success, happiness, and a sense of achievement, forming a positive motivation and enhancing their self-efficacy. The corresponding operating equipment should be improved and technical personnel should be equipped for guidance, so that tea farmers can preliminarily practice the digital knowledge and skills they have learned, deeply realize the value of digital knowledge and skills, personally experience the benefits of improving digital literacy, enhance self-efficacy, and become more confident and motivated to improve their digital literacy in the future.

7.3. Expand Training Types, Channels, and Forms to Enhance Tea Farmers’ Expectancy for Hard Work

The government can collaborate with universities, research institutions, and other parties to establish a digital literacy training team to provide multi-level and personalized digital knowledge and skills training for tea farmers, meet diversified needs, explore more efficient digital literacy training solutions, and collaborate with enterprises to develop smart agriculture related application software or digital technologies that are easy to understand and operate for tea farmers, thereby lowering the threshold for tea farmers to participate in digitization. Using smartphone terminals as a support, providing remote guidance and assistance for tea farmers to improve their digital literacy, forming a dynamic coordination and assistance mechanism for time and space, and using live streaming, video, or animations to assist tea farmers in clearing difficulties and obstacles can promote the development of a digital literacy training model that combines online and offline, theory and practice. Guided by the intelligent transformation and development of the tea industry, a digital literacy training models can effectively connect stage training, continuous training, and systematic training for tea farmers, build a “digital tea farmer” training base that integrates industry and education, carry out “field teaching”, strengthen the mastery of digital knowledge and skills, and improve the practical level of digital application.

7.4. Promote the Inclusive Effect and Practical Value of Digital Intelligence Empowerment, and Enhance the Performance Expectancy of Tea Farmers

The government or relevant organizations should make tea farmers truly aware of the usefulness and benefits of improving digital literacy for promoting the development of the digital empowering tea industry. In the process of popularization, specific practical cases can be combined to enhance tea farmers’ effective understanding of the development model of smart agriculture, making them aware that having good digital knowledge and skills will help upgrade tea planting, production, and other links. To achieve intelligent management of tea gardens, intelligent equipment can be applied in tea activities and valuable information can be obtained. Through intelligent equipment or technology, benefits such as energy reduction, quality improvement, yield increases, and income increases can be achieved, enhancing the performance expectancy of tea farmers.

7.5. Explore Individual Innovative Tea Farmers and Cultivate Digital Talents in the Tea Industry

The government, tea cooperatives, and tea associations can explore individual tea farmers with high innovation and certain characteristics, encourage them to develop the tea industry through smart agriculture models, and provide policy and financial support for tea farmers to use smart agriculture technology or equipment. These groups can organize tea farmers with high individual innovation to focus on training, improve the training and incentive mechanism for digital talents in the tea industry, hold digital knowledge and skills competitions, select and evaluate digital talents in the tea industry, cultivate a group of tea farmer elites, leverage the “head goose effect” of such tea farmers to drive surrounding tea farmers to improve their digital literacy, and promote the transformation of tea farmers’ digital literacy into productivity and creativity for the intelligent development of the tea industry, cultivating batch after batch of digital talents for the intelligent development of the tea industry.

7.6. Strengthen Policy Support and Assistance Guarantees, and Optimize the Convenient Conditions for Tea Farmers to Enhance Their Digital Literacy

The government should strengthen policy support and training cost subsidies for tea farmers’ digital literacy assistance and encourage economically capable tea farmers to improve their digital literacy. Additionally, it should emphasize the implementation of the teaching staff of institutions or organizations that value digital literacy training, provide policy support or subsidies for digital literacy training-related institutions or organizations, establish supervision and incentive mechanisms, provide guarantees and support for tea farmers’ digital literacy training, and establish and improve a long-term mechanism for cultivating tea farmers’ digital literacy. The improvement of tea farmers’ digital literacy is a dynamic and iterative process, coupled with the rapid development of technology. It is necessary to adjust and optimize the digital literacy cultivation mechanism based on the intelligent development process of the tea industry and the status of tea farmers’ digital literacy cultivation in order to create convenience for the improvement of tea farmers’ digital literacy. The government and relevant institutions should track and serve the training effectiveness of moderately scaled tea farmers, and upgrade and standardize the training based on feedback from tea farmers.

8. Limitations and Future Studies

Although this study analyzed the influencing factors of tea farmers’ digital literacy improvement behavior in moderately scaled operation through standardized empirical research methods, summarized the results of empirical research analysis, and proposed corresponding countermeasures for tea farmers’ digital literacy improvement based on empirical conclusions, this study may have exhibited the following limitations due to limited personal academic research experience and limited time and energy investments in tea farmers’ digital literacy research.

Firstly, there may have been shortcomings in the empirical analysis section of this study in terms of the variables and measurements of the scale dimensions. This study retained the four main variables of the UTAUT theoretical model and introduced two new variables: individual innovation and self-efficacy. In addition to the variables involved in this study, there may have been other variables that affected the improvement of tea farmers’ digital literacy. Although the scale design of the variables fully considered the characteristics of tea farmers and was based on the original scale and relevant literature, there may have been a problem where the setting of the question items was not detailed enough.

The second reason was that the scope of the research sample in this study may not have been sufficient. The area investigated in this study was Anxi County, the main tea producing county in China. Anxi County is a world-famous tea producing county. At present, the tea industry is gradually and intelligently developing. Considering its representativeness, a total of 440 samples were collected through the investigation of 11 major tea towns in Anxi County. Although it met the requirements of the structural equation model for the number of samples, the research results may have been affected to some extent due to the scope of the research area.

In the process of the intelligent development of the tea industry, the digital literacy of tea farmers is key. Improving the digital literacy of tea farmers can better integrate the intelligent development of the tea industry and empower the development of the tea industry through digital intelligence. In order to better promote the improvement of tea farmers’ digital literacy, combined with the current shortcomings of this study, future research on tea farmers’ digital literacy can be further improved from the following two aspects. Firstly, when conducting research on the influencing factors of the tea farmers’ willingness and behavior to improve their digital literacy, other influencing factors can be explored to investigate whether there are other factors or other mediating and moderating variables that have an impact, and more in-depth research on the willingness and influencing factors of tea farmers to improve their digital literacy can be conducted. Secondly, different tea producing areas may have different characteristics. Future research can expand the coverage of the samples and conduct research on tea producing areas in different places, so that the research conclusions can better guide the practice of improving tea farmers’ digital literacy.