Research on Countermeasures for Improving the Digital Literacy Level of Moderate-Scale Tea Farmers

Abstract

In the context of smart agriculture, the tea industry is undergoing a transformative shift toward intelligent development. As the birthplace of tea, China holds a significant position in the global tea industry, with Anxi County in Quanzhou City, Fujian Province—renowned as the origin of Tie Guan Yin—standing as the world’s largest oolong tea production area. Its intelligent transformation of the tea industry is typical and representative. However, current research on the digital literacy of farmers is not yet mature, and there is a lack of systematic research on this specific group of tea farmers, which to some extent restricts the transformation of the tea industry towards intelligent development. The level of digital literacy among tea farmers is crucial for the intelligent development and transformation of the tea industry. Improving the digital literacy of tea farmers is the key to promoting the intelligent development of the tea industry. Therefore, studying the digital literacy of tea farmers has significant practical significance. This article takes Anxi County as the research area and focuses on moderate-scale tea farmers as the research object. Based on the United Nations Global Framework for Digital Literacy and taking into account the actual situation of tea farmers, an evaluation index system and analysis framework for tea farmers’ digital literacy have been constructed from seven dimensions: equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, problem-solving literacy, and professional digital literacy. Using literature review, questionnaire survey, interview, and quantitative analysis methods, a questionnaire containing the above-mentioned dimensions was designed. After collecting data, the rationality of the questionnaire structure was verified using SPSS software. The digital literacy level of 440 medium-sized tea farmers from 11 major tea-producing townships in Anxi County was measured, analyzed, and Two-Tailed correlation tests were conducted. The results indicate that there are currently six aspects of digital literacy among tea farmers that are at a moderate level, and professional digital literacy is the weakest among the seven digital literacy. The overall digital literacy level of tea farmers needs to be strengthened. Large-scale tea farmers have the conditions to apply smart agricultural equipment and technology, which can achieve intelligent and refined management of tea gardens and intelligent upgrading of the entire industry chain. Based on the research results of the seven digital literacy of tea farmers, this article proposes improvement measures corresponding to the seven digital literacy of tea farmers from the perspectives of “government, industry associations, and training institutions”, providing reference for Anxi County and other tea-producing areas in the world.

1. Introduction

In today’s era, smart agriculture, as an innovative model of cross-border integration of digital information technology and agriculture, is fully empowering the entire agricultural industry chain with digital information and intelligent technology. It permeates decision-making, planting, production, management, transportation, and sales, driving agriculture toward intelligence, refinement, visualization, remote operation, efficiency, and digitization. This transformation significantly enhances agricultural productivity, becoming an inevitable trend in global agricultural modernization. Reshaping the global agricultural landscape with an average annual efficiency improvement of 12%, it underscores the critical role of digital transformation. According to the Food and Agriculture Organization (FAO), farms adopting intelligent irrigation systems achieve 40% higher water resource utilization than traditional models. IoT technology has enabled pest and disease warning accuracy to exceed 85%. The global smart agriculture market is projected to reach $30 billion by 2025. As a pivotal branch of agricultural intelligent transformation, China’s tea industry leads globally, accounting for 60% of world tea production. However, its overall digital application level remains underdeveloped. Anxi County in Fujian Province exemplifies this challenge: as the world’s largest oolong tea production area, it boasts 600,000 acres of tea gardens, a millennium-long tea production history, and an annual output value exceeding ¥28 billion. Yet, its digital penetration rate is below 35%. Despite being recognized as a national smart tea industry demonstration area via the “Tea Technology” strategy, tea farmers’ digital skills gap limits smart device utilization to just 42%, directly constraining precision management and e-commerce sales efficiency in tea gardens. Anxi County Government data indicates that by 2025, the county has established 4 provincial-level smart agricultural parks and 15 municipal-level agricultural IoT demonstration sites. For moderate-scale tea farms (comprising 40% of the county’s total tea garden area), IoT environmental monitoring proficiency stands at 35–40%, while basic e-commerce live-streaming adoption is 20–30% (less than 15% for advanced skills). A longitudinal study by Fujian Agriculture and Forestry University reveals that tea farmers applying smart agriculture models achieve 18–25% higher unit area output value than the average. For example, 127 tea farmers in the Baima Tea Industry Smart Tea Garden Demonstration Base have achieved a 19.8-fold increase in production efficiency and a 28.3% decrease in energy consumption through 5G+IoT technology. However, such cases only cover 15% of moderate-scale tea farmers in the county, and the improvement in digital literacy among moderate-scale tea farmers plays a key role in the implementation of smart agriculture technology. Although Anxi County has been recognized as a model area for China’s smart tea industry through its “tea technology” strategy, the lack of digital skills among tea farmers has resulted in a usage rate of only 42% for smart devices, directly restricting the efficiency release of precision management and e-commerce sales in tea gardens. This contradiction of “sufficient technology supply but lagging human capital” has made Anxi County a typical example of observing the bottleneck of the smart transformation of the tea industry [1,2,3,4,5,6,7,8,9]. Currently, the uneven level of digital literacy among tea farmers has become a core obstacle to the widespread application of digital technology and intelligent devices, thereby delaying the overall pace of transformation. Therefore, studying tea farmers’ digital literacy in Anxi County not only provides practical solutions to address the “last mile” technology adoption challenge but also enriches the theoretical framework of smart agriculture transformation. It provides a reference for improving human capital for major tea-producing developing countries around the world and has significant practical value and theoretical significance [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15].

Scholars both domestically and internationally have conducted extensive research on digital literacy. Regarding research agendas, most studies focus on constructing digital literacy assessment frameworks, designing evaluation indicator systems, and developing measurement methodologies. The conceptualization of digital literacy has matured significantly, with consensus defining it as an individual’s capacity to utilize digital technology for acquiring, evaluating, utilizing, and creating information in digital environments—encompassing dimensions such as digital awareness, knowledge, skills, and ethics. For framework construction, research predominantly draws upon the EU Digital Literacy Framework and the United Nations Global Framework for Digital Literacy [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]. The former is mostly applied to the study of people’s groups in developed countries, while the latter focuses on developing countries. Scholars construct the framework based on the characteristics of different research subjects. From the perspective of research subjects, it mainly focuses on groups such as students, teachers, civil servants, and library staff. Research on the farmer group is relatively scarce and started relatively late. Research on constructing a digital literacy evaluation index system and measurement for the tea farmer group is even rarer, and there is a significant gap in systematic research on this specific group of tea farmers. Matt et al. pointed out in their study on digital transformation of small farmers in South Africa that digital literacy education needs to be combined with practical scenarios such as mobile applications, precision agriculture tools, and e-commerce platforms. Li et al. empirically found through 1116 samples of Chinese farmers that high digital literacy groups can improve agricultural green production efficiency by 18–25% by integrating physical chemical biological comprehensive prevention and control technologies. Wu Xiaolong and Wang Han proposed from the perspective of competency theory that farmers’ digital literacy should include nine dimensions: digital acquisition, digital agriculture, and digital security, emphasizing its driving effect in rural digital economy and digital governance [10,11,12,13,14,15]. Methodologically, questionnaire surveys are commonly employed to collect data, with instruments designed to assess dimensions like digital knowledge, skills, and awareness. Some studies complement surveys with interviews to deepen understanding of farmers’ challenges and needs in applying digital technologies. Case analysis is also used, selecting representative regions or agricultural projects to analyze how improved farmers’ digital literacy impacts agricultural production efficiency and economic benefits [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]. Regarding research content, most studies adapt existing frameworks to address challenges and strategies for enhancing farmers’ digital literacy within digital rural construction and digital agriculture development, with limited focus on measurement frameworks [13,14,15,16,17,18,19,20,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38]. From a research perspective, most studies mainly focus on problem-solving at the practical application level. However, there are certain shortcomings in current research, as the study of digital literacy among farmers is not yet mature, and there is a lack of systematic research targeting this specific group of tea farmers. The innovation of this study lies in its adaptation of the “United Nations Global Framework for Digital Literacy” to develop a moderate-scale tea farmer digital literacy questionnaire for measurement [8,9,10,11,12,13,14,15,16,17,35,36,37,38,39,40]. On the one hand, it improves the universality of the framework for different research groups in developing countries. On the other hand, it evaluates and analyzes the digital literacy level of tea farmers in Anxi County, a world-renowned tea-producing area, proposes targeted improvement measures, and enriches the research field of farmers’ digital literacy measurement framework [9,10,11,12,13,14,15,16,17,18,19].

For Anxi County, this study has important practical significance and innovative value. At present, research on the digital literacy of farmers is not yet mature, and systematic research on this specific group of tea farmers is even more lacking [2,3,4,5,6,7,8,9,10,11,12,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]. In contrast to prior studies on digital literacy of farmers, such as Wang et al.’s 2024 study on digitalization of rural areas in China and Zhang Lin et al.’s (2025) study based on 1756 survey data from five central and western provinces, a six dimensional evaluation system for digital literacy of rural residents was constructed to reveal the differences and improvement mechanisms of digital literacy levels among different types of farmers. This study deeply integrates the United Nations framework with the characteristics of the tea industry for the first time. Based on the “United Nations Global Framework for Digital Literacy” and combined with the actual situation of moderate-scale tea farmers in Anxi County, a digital literacy evaluation index system and analysis framework for tea farmers were constructed, and empirical measurement and analysis were carried out [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,29,39]. On the one hand, it has improved the universality of the framework for specific research groups (tea farmers) in developing countries, filling the gap in systematic research on tea farmers’ digital literacy; on the other hand, by accurately evaluating the digital literacy level of tea farmers in Anxi County and proposing targeted improvement measures, practical solutions have been provided to solve the problem of “difficult technology landing” in the intelligent transformation of the tea industry. This study has important practical significance for the intelligent transformation of the tea industry in Anxi County, the improvement in tea farmers’ human capital, and the sustainable development of the global tea industry [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,34,35,36,37,38,39,40,41,42,43,44,45].

2. Theoretical Framework Construction

The UNESCO Global Framework for Digital Literacy provides a widely applicable reference paradigm for digital literacy research. This framework is based on the EU Digital Literacy Framework and is designed to be adaptable to digital literacy research across countries at various stages of development, particularly addressing the needs of developing nations. It establishes a primary indicator dimension comprising seven first-level indicators and 26 second-level indicators across domains including software and equipment operation skills, information and data literacy, communication and collaboration, digital content creation, security, problem-solving ability, and job-related competencies [9,10,11,12,13,14,15,16]. Building on the EU framework’s dimensions, the UNESCO model incorporates additional dimensions for software/equipment operation skills and job-related abilities. This expansion enhances its flexibility for research involving specific occupational or industry-specific groups, allowing indicators to be tailored according to the unique characteristics of the research subjects. The structure of this framework supports cross contextual applications while maintaining methodological rigor, making it particularly suitable for research focused on professional or industry-specific populations [8,9,10,11,12,14,15,16].

In view of this, this study fully drew on the essence of the “Global Framework for Digital Literacy” when constructing a questionnaire for assessing the digital literacy of tea farmers engaged in moderate-scale operations. Firstly, the study conducts a thorough analysis of the connotations and logical relationships among the seven primary indicators and 26 secondary indicators within the framework to clarify their comprehensiveness and systematicity in measuring digital literacy. Secondly, drawing from relevant research literature on farmers’ digital literacy, the study summarizes the key points and common dimensions from previous evaluations of farmers’ digital literacy, thereby providing empirical references for assessing tea farmers’ digital literacy [9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]. At the same time, the study conducts on-site preliminary research to gain a deeper understanding of the actual situation and specific needs of moderate-scale tea farmers in Anxi County in terms of digital technology application, information acquisition and processing, communication and collaboration.

Based on the above work, this study focuses on tea farmers’ abilities to operate smart agriculture related equipment and software from the perspectives of device and software operation skills (corresponding to the dimensions of software and device operation skills in the framework), digital information literacy (covering the dimensions of information and data literacy, examining tea farmers’ abilities to obtain, analyze, and utilize information related to the tea industry), digital communication and collaboration literacy (combining the dimensions of communication and collaboration, focusing on tea farmers’ ability to communicate and cooperate with others in the digital environment), digital content creation literacy (corresponding to the dimensions of digital content creation, evaluating tea farmers’ ability to create digital content related to the tea industry), digital security literacy (understanding tea farmers’ understanding and prevention ability of digital security based on the security dimension). A digital literacy evaluation index system and analysis framework suitable for moderate-scale tea farmers in Anxi County were constructed based on seven dimensions: problem-solving literacy (referring to the dimension of problem-solving ability, analyzing the ability of tea farmers to use digital technology to solve practical problems in the tea industry) and professional digital literacy (highlighting the dimension of professional related abilities, emphasizing the ability of tea farmers to apply digital technology in tea industry professional scenarios). The evaluation questionnaire was set to ensure the scientific and targeted nature of the evaluation.

Table 1. UNESCO global framework for digital literacy.

First Level Indicators	Secondary Indicators	Indicator Description
Software and device operation skills	Physical operation of digital devices	Identify and utilize the functions and features of hardware tools and technologies.
	Software operation in digital devices	Know and understand the data, information, and digital content required to operate software tools and techniques.
Information and Data Literacy	Browse, search, and filter data, information, and digital content	Clarify information needs, search for data, information, and content in the digital environment, access them and navigate between them, create and update personal search strategies.
	Evaluate data, information, and digital content	To analyze, compare, and critically evaluate the credibility and reliability of data, information, and digital content sources, analyze, interpret, and critically evaluate data, information, and digital content.
	Manage data, information, and digital content	Organize, store, and retrieve data, information, and content in a digital environment, and organize and process them in a structured environment.
Communication and collaboration	Interacting through digital technology	Interact through various digital technologies suitable for specific platforms.
	Sharing through digital technology	Share data, information, and digital content with others through appropriate digital technologies.
	Participate in citizen work through digital technology	By utilizing public and private digital services to participate in society, seeking opportunities for self-empowerment and citizen engagement through appropriate digital technologies.
	Collaboration through digital technology	Utilize digital tools and technologies for collaborative processes, as well as for the co construction and co creation of resources and knowledge.
	Digital culture	When using digital technology and interacting in a digital environment, it is important to understand behavioral norms and proprietary technologies, adapt communication strategies to specific audiences, and be aware of cultural diversity in the digital environment.
	Manage Digital Identity	Create and manage one or more digital identities to protect one’s reputation and handle data generated through multiple digital tools, environments, and services.
Digital Content Creation	Developing digital content	Create and edit digital content in different formats to express oneself digitally.
	Integrate and rewrite digital content	Modify, refine, improve, and integrate information and content into existing knowledge systems to create new, relevant content and knowledge.
	Digital copyright	Understand how copyright and licensing apply to data, information, and digital content.
	Plan and design	Plan and develop a series of easily understandable programs for computing systems to solve given problems or perform specific tasks.
Secure	Protection equipment	Protect devices and digital content, understand risks and threats in the digital environment, understand security measures, and fully consider reliability and privacy.
	Protecting personal data and privacy	Protecting personal data and privacy in the digital environment; Understand how to use and share personal identity information while protecting oneself and others from harm; Understand how digital services use the ‘Privacy Policy’ to inform how personal data is used.
	Protecting physical and mental health	Being able to avoid health risks and threats to physical and mental health when using digital technology; Being able to protect oneself and others from potential harm in the digital environment (such as cyberbullying); Understand the impact of digital technology on social welfare and social inclusion.
	Protect the environment	Be aware of the impact of digital technology and its use on the environment.
Problem-solving ability	Resolve technical issues	Understand and solve technical issues encountered when operating devices and using digital environments (from troubleshooting to solving more complex problems).
	Identify requirements and technical countermeasures	Assess needs and identify, evaluate, select and use digital tools and potential technological responses to address them; Adjust and customize the digital environment based on individual needs, such as accessibility.
	Creatively utilizing digital technology	Using digital tools and technologies to create knowledge and innovate processes and products; Can participate in cognitive processing individually and collectively to understand and solve conceptual problems in the digital environment.
	Determine the digital permission gap	Understand areas where one needs to improve or update their digital abilities; Being able to help others through the development of digital abilities; Seeking opportunities for self-development and keeping up with the times.
	Computational thinking	Decompose computable problems into sequential and logical steps as solutions for both humans and computer systems.
Occupational related abilities	Specialized digital technology for operations in specific fields	Identify and utilize specialized digital tools and technologies in specific fields.
	Interpret and manipulate data, information, and digital content in specific fields	Understand, analyze, and evaluate specialized data, information, and digital content in specific fields of the digital environment.

Note: The table is sourced from the UNESCO Global Framework for Digital Literacy.

below presents the UNESCO Global Framework for Digital Literacy [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18].

3. Research Methods

Based on existing literature on digital literacy and the UNESCO Global Framework for Digital Literacy, this study adhered to the principles of objectivity, scientific rigor, guidance, and operability to design a survey questionnaire investigating the current digital literacy status of tea farmers in Anxi County—a major tea-producing region in China. The survey mainly includes two parts: the personal situation of tea farmers operating on a moderate scale, and the digital literacy level of tea farmers operating on a moderate scale [9,10,11,12,13,14,15,16,17,18,19,25,28,29,30,31,32,33].

3.1. Questionnaire Preparation

This article draws on the United Nations’ “Global Framework for Digital Literacy” and develops dimensions and survey questionnaires that reflect the digital literacy of tea farmers based on their characteristics (see

Table 2. Items of the digital literacy measurement scale for tea farmers.

Dimensions	Objective Question Items
Equipment and software operation skills (A)	A1: I am familiar with digital technologies such as big data, the Internet of Things, artificial intelligence, wireless Wi Fi, 4G/5G, etc.
	A2: I am proficient in using general functions of smartphones, such as making phone calls, sending text messages, installing software, and electronic payments.
	A3: I am proficient in using general computer functions such as typing, browsing the web, sending emails, and online shopping.
	A4: I can understand the operation interface of mobile/computer software or pages.
	A5: I am able to use the applications installed on my phone/computer well.
	A6: I am able to operate the connection and interactive use between digital devices (such as the connection between wearable devices and mobile phones, mobile phones and computers, etc.).
Digital Information Literacy (B)	B1: I can browse the tea information website or WeChat official account, etc.
	B2: I am proficient in using my phone/computer to search for information that I need or am interested in.
	B3: I can download information related to tea.
	B4: I can distinguish the authenticity of information obtained online.
Digital Communication and Collaboration Literacy (C)	C1: I can exchange tea related topics with others on WeChat, Tiktok, Kwai and other platforms.
	C2: I can publish comments on tea and interact with others on WeChat, Tiktok, Kwai and other platforms.
	C3: I am proficient in sharing links, files, images, audio and video to others in different ways on the internet.
	C4: I am able to coordinate or collaborate with others on tea related work through online communication.
	C5: I can communicate well with customers when selling tea on online platforms.
	C6: When communicating online, I can achieve civilized communication.
Digital Content Creation Literacy (D)	D1: I can shoot audio, video, pictures, etc., related to tea based on my own ideas.
	D2: I can use software to edit existing audio, video, images, etc., according to my own ideas.
	D3: I will use online platforms for live streaming about tea.
	D4: I will publish my digital tea content works on WeChat groups, circles of friends, Tiktok, Kwai and other platforms.
	D5: I understand how to comply with digital copyright (such as indicating the source when making audio and video).
Digital Security Literacy (E)	E1: I understand that there are security risks in the digital network environment.
	E2: I can protect digital devices by installing protective software.
	E3: I am able to securely access and utilize digital infrastructure such as networks (such as avoiding connecting to unfamiliar Wi Fi or accessing insecure websites).
	E4: I can maintain the security of online funds and not easily transfer money to others.
	E5: I can take measures to protect personal data and privacy (such as setting confidentiality, not easily disclosing personal information, etc.).
	E6: I am able to protect myself and others’ interests from harm in the digital environment (such as cyberbullying and online fraud).
Problem-solving literacy (F)	F1: I am able to solve basic problems in the use of digital device products.
	F2: When a digital device malfunctions, I can assess the difficulty of repair (whether I can fix it myself).
	F3: I am good at using digital devices such as mobile phones/computers to identify problems encountered.
	F4: I am good at using digital devices such as mobile phones/computers to find solutions to problems.
	F5: I am able to reflect on my own shortcomings in digital skills and actively improve them.
Professional digital literacy (G)	G1: I can use my phone/computer to search for information on tea cultivation, production, or processing.
	G2: I can use my mobile phone/computer to find solutions to problems such as tea planting, production, or processing.
	G3: I am able to independently learn knowledge related to tea through my phone/computer.
	G4: I can use artificial intelligence technology for monitoring in tea garden management (such as intelligent monitoring of tea garden meteorology, soil, pests and diseases, etc.).
	G5: I can use crop protection drones in tea garden management.
	G6: I can use intelligent picking equipment to pick tea leaves.
	G7: I can use the fertilizer water intelligent irrigation system to manage tea gardens.
	G8: I can apply secure anti-counterfeiting traceability technology to tea.
	G9: I am able to use corresponding intelligent processing equipment in the tea processing and production process.
	G10: I am able to sell tea products through digital e-commerce platforms.

Note: UNESCO Global Framework for Digital Literacy, Ma Li, Yang Yanmei, Su Lanlan, Zhang Hangyu, Peng Yanling, Wu Xiaolong, etc. [9,10,11,12,13,14,15,16,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55] (Source reference).

). The questionnaire is designed based on dimensions and scored using the Likert five-point scale. After forming the initial framework and questionnaire, tea industry experts are consulted to guide, demonstrate, and test the content of the framework and questionnaire multiple times. After conducting pre research on some questionnaires, the framework and current status survey questionnaire for tea farmers’ digital literacy are finally formed to ensure a better understanding of the current situation and problems of tea farmers’ digital literacy. According to the seven primary indicators of the “Global Framework for Digital Literacy”, this study also sets the measurement dimensions of tea farmers’ digital literacy to seven, namely “equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, problem-solving literacy, and professional digital literacy”, corresponding to a total of 42 objective items. Table 2 shows the measurement items of the survey questionnaire on the current status of digital literacy among tea farmers [9,10,11,12,13,14,15,16,17,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35].

3.2. Research Object

This study conducted formal research on tea farmers in Anxi County, Fujian Province, China. Tea farmers came from 11 main tea-producing 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” in the contiguous tea area of Anxi County.

3.3. Research Plan Design

Several scholars have conducted research on moderate-scale operations. Chen Gongmiao (1987) proposed 4.5–9 acres as the suitable scale for 2 labor force tea farmers [56]; Zhong Guangming (1989) suggested from the perspective of labor force that the per capita area should be 4.5–5 acres [57]; Liu Fuzhi et al. (1995) found through analysis of yield, output value, and profit that the average labor force is 4.5–6 acres [58]; Fang Qing (2002) used fuzzy mathematics and analytic hierarchy process to find that 10–15 acres can achieve the best scale benefits, and can be moderately expanded to 20–25 acres [59]; Zhu Yin (2013) proposed 32 acres as the optimal area based on pesticide use behavior research; Cheng Jinning (2014) pointed out that the scale of tea farming in Anxi County is mostly concentrated in 30–50 acres, and around 30 acres can be declared as a family farm [60]; Sun Tong (2021) calculated that a moderate scale is around 40 acres using the DEA-AHP method [61]. This study integrates the above research and the indicators for determining the appropriate scale of land, and explicitly takes the tea plantation operating area of 20–50 acres as the appropriate scale range, and uses it as the core survey object. It emphasizes that this range can achieve the best economic benefits under the optimization of factor allocation. Based on the research of domestic and foreign scholars, this paper takes the “land scale” as the indicator to judge whether the tea farmers’ tea gardens are suitable for large-scale operation, and takes the tea farmers’ tea gardens’ operating area of about 1.3–3.3 hectares (the surveyed tea farmers’ tea gardens’ planting scale is between 20–50 acres and relatively contiguous) as the land scale range of tea gardens’ suitable for large-scale operation [40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62]. At the same time, it defines the tea farmers’ digital literacy as that tea farmers understand the Internet, the Internet of Things, big data, cloud computing and other modern information technologies in the digital context, can obtain and identify digital information, carry out digital communication and collaboration, and create digital content, can use digital equipment, have a good sense of digital security, and apply digital technology and Digital knowledge and skills required to use smart devices. This article draws on the United Nations’ “Global Framework for Digital Literacy” and develops dimensions and survey questionnaires that reflect the digital literacy of tea farmers based on their characteristics. The questionnaire is designed according to the dimensions, and the items are scored using the Likert five-point scale. After forming the initial framework and questionnaire, we asked tea industry experts from Fujian Agriculture and Forestry University to guide, demonstrate, and test the content of the framework and questionnaire. The questionnaire was sent to moderately sized tea farmers of different genders, ages, and educational backgrounds through online and offline methods for pre research and then improved to ultimately form a framework and current status survey questionnaire for tea farmers’ digital literacy, in order to ensure a better understanding of the current situation and problems of tea farmers’ digital literacy. Then, 11 moderately sized tea farmers from major tea-producing towns in Anxi County were selected as research subjects, and questionnaires were distributed to collect data. By using literature analysis, questionnaire survey, interview, and quantitative analysis methods, data on the digital literacy questionnaire of tea farmers were collected. Then, SPSS software (26.0 version) was used for data statistics, testing, and analysis to evaluate the digital literacy level of moderate-scale tea farmers in Anxi County. Finally, based on the analysis results, targeted measures are proposed from different dimensions to enhance the digital literacy of tea farmers.

3.4. Data Collection

The survey of tea farmers was conducted from 10 September 2022 to 27 November 2022. A total of 464 questionnaires were collected and sorted out. After removing invalid questionnaires according to the rules, 440 valid questionnaires were obtained, with a valid questionnaire rate of 94.8%. The required sample size ensures good iteration and convergence during analysis.

4. Analysis of Research Results

4.1. Verification of Tea Farmers’ Digital Literacy Survey Questionnaire

Before analyzing the current status of tea farmers’ digital literacy level, the data from the digital literacy level survey questionnaire will be subjected to reliability testing, followed by KMO and Bartlett sphericity tests to ensure the reliability of the collected data (see

Table 3. Reliability testing, KMO and Bartlett sphericity testing. (a) Reliability test coefficients of the tea farmers’ digital literacy scale, (b) KMO and Bartlett spherical test of the tea farmers’ digital literacy scale.

(a)
Dimensions	Cronbach’s α	Overall Cronbach’s α
Equipment and software operation skills	0.892	0.971
Digital Information Literacy	0.825
Digital Communication and Collaboration Literacy	0.809
Digital Content Creation Literacy	0.827
Digital Security Literacy	0.743
Problem-solving literacy	0.873
Professional digital literacy	0.894
(b)
Dimensions	KMO Value	Bartlett Sphericity Test
		Approximate Chi Square	df.	Sig.
Equipment and software operation skills	0.892	1476.063	15	0.000
Digital Information Literacy	0.783	731.122	6	0.000
Digital Communication and Collaboration Literacy	0.836	797.446	15	0.000
Digital Content Creation Literacy	0.841	750.934	10	0.000
Digital Security Literacy	0.811	516.900	15	0.000
Problem-solving literacy	0.772	1553.344	10	0.000
Professional digital literacy	0.898	2577.387	45	0.000
Overall situation of dimensions	0.97	13,189.237	861	0.000

a,b).

Reliability mainly reflects the degree of consistency and stability of the collected sample data in repeated tests. If the reliability is higher, it indicates that the collected sample data is more reliable. At present, there are many methods used for reliability testing, but the widely used reliability calculation method is Cronbach’s alpha coefficient. The Cronbach’s alpha coefficient is between 0 and 1. Generally, when the alpha coefficient is less than 0.7, it indicates that the scale is not reliable; When the alpha coefficient is greater than or equal to 0.7 and less than 0.8, it indicates that the scale has a certain level of reliability and can be used for research and analysis; When the alpha coefficient is greater than or equal to 0.8 and less than 0.9, it indicates good reliability of the scale; When the alpha coefficient is greater than or equal to 0.9 and less than 1, it indicates excellent reliability of the scale. When the Cronbach’s alpha coefficient is greater than 0.6, it indicates that the scale reliability is within an acceptable range. When the Cronbach’s alpha coefficient is greater than 0.8, it indicates that the scale reliability is good and the sample data reliability is strong. The overall Cronbach’s alpha of the survey questionnaire on the digital literacy status of tea farmers in this study is 0.97, and the Cronbach’s alpha values corresponding to all seven dimensions are greater than 0.7, indicating that the survey questionnaire is acceptable and has high reliability [6,7,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26,27,28,29,30] (See Table 3a).

Validity mainly reflects the degree to which the survey questionnaire correctly and effectively measures the validated features corresponding to the measurement indicators. If the validity of the survey questionnaire is higher, it indicates that the data obtained from the survey questionnaire has a high consistency with the items we want to investigate and can accurately reflect the characteristics to be measured in the research. This study conducted exploratory factor analysis on 440 collected sample data and used SPSS software to explore the effectiveness of the questionnaire structure developed. Exploratory factor analysis mainly involves conducting KMO and Bartlett sphericity tests. Generally, in KMO and Bartlett sphericity tests, the KMO value needs to be greater than 0.7, and the significance level of Bartlett sphericity test should be less than 0.001. Through testing, the KMO value of the survey questionnaire on the current status of tea farmers’ digital literacy in this study is 0.97, and the KMO values of the seven dimensions in the questionnaire are also greater than 0.7, which meets the KMO value requirements. At the same time, the significance level of each dimension and the Bartlett sphericity test of the total scale is less than 0.001, which meets the Bartlett sphericity test requirements. The total variance explanatory power reaches 62.14%, indicating that the scale has good structural validity, and the validity and correlation between each item are good [17,18,19,20,21,22,23,24,25,63] (See Table 3b).

4.2. Descriptive Statistics

Based on the data obtained from previous research and investigation, we have learned about the basic situation of tea farmers and the future introduction or development of smart agricultural equipment and technology [30]. According to

Table 4. Personal basic information of tea farmers.

Basic Information	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%
	56 years old and above	18	4.10%
Educational level	Primary school	48	10.90%
	Junior high school	153	34.80%
	High school and vocational school	163	37.00%
	College diploma	63	14.30%
	Bachelor’s degree or above	13	3.00%
Do you want to join the tea cooperative?	Yes	263	59.80%
	No	177	40.20%
Have you participated in a farmer training program?	Yes	51	11.60%
	No	389	88.40%
Engaged in tea planting time	2–5 years	7	1.60%
	6–10 years	146	33.20%
	11–15 years	152	34.50%
	16–20 years	79	18.00%
	21–25 years	46	10.50%
	Over 26 years	10	2.30%
Operating tea plantation area	20–25 acres	193	43.90%
	26–30 acres	151	34.30%
	31–35 acres	64	14.50%
	36–40 acres	21	4.80%
	41–45 acres	7	1.60%
	46–50 acres	4	0.90%
Daily usage time of electronic products	(0, 2) h	89	20.20%
	[2, 4) h	329	74.80%
	More than 4 h	22	5.00%
Understanding the situation of smart agriculture	Fully understood	5	1.10%
	Basic understanding	75	17.00%
	Generally	213	48.40%
	I don’t quite understand.	146	33.20%
	I have no idea at all.	1	0.20%

, in the 440-point sample data, there is not much difference in the proportion of male and female respondents. The majority of tea farmers are aged between 26 and 45 years old, and their education is mostly at the junior high school, high school, or vocational school level. The proportion of college and university degrees is relatively small. 59.8% of tea farmers report membership in tea cooperatives, while over 88.4% indicate no participation in formal farmer training programs. Over half of the surveyed farmers have 6–15 years of experience in tea cultivation. The number of tea farmers with relatively contiguous planting areas of 20–30 acres is relatively large, and nearly three-quarters of tea farmers use electronic products for 2–4 h a day. Most tea farmers are not particularly familiar with the development of smart agriculture [30].

According to

Table 5. The situation of smart agricultural equipment/technologies that tea farmers hope to introduce or develop in the future.

Tea Farmers Hope to Introduce or Develop Smart Agricultural Equipment/Technology in the Future	Response		Percentage of Cases (Prevalence Rate) n = 440
	Number of Cases (N)	% (Response Rate)
Tea picking robot	422	19.60%	95.91%
Tea Garden Monitoring System (Tea Garden Monitoring, Monitoring of Meteorology, Soil, Diseases and Pests, etc.)	409	19.00%	92.95%
Automatic irrigation system for water and fertilizer in tea gardens	170	7.90%	38.64%
Drone defense technology for tea gardens	311	14.50%	70.68%
Tea garden frost prevention facilities	232	10.80%	52.73%
Tea anti-counterfeiting traceability technology	204	9.50%	46.36%
Precision processing technology for tea	263	12.20%	59.77%
Digital Marketing Technology for Tea sales	138	6.40%	31.36%
Total	2149	100.00%	488.41%

, tea farmers recognize the development trend of smart agricultural equipment and technology [30]. The popularity rate of tea farmers wanting to introduce “intelligent tea picking robots” is the highest, followed by “intelligent monitoring systems for tea gardens”, both of which have a popularity rate of over 90%. The popularity rates of “tea garden drone flight defense technology”, “tea intelligent fine processing technology”, and “tea garden frost prevention facilities” have all exceeded 50%. The popularity rates of “tea anti-counterfeiting traceability technology”, “tea garden water and fertilizer automatic irrigation system”, and “tea digital marketing technology” have also exceeded 30%. From this, it can be seen that most tea farmers hope to introduce smart agricultural equipment and technology to the planting and production processing stages of the entire tea industry in the future. They hope to upgrade tea plantation management and production processing by introducing advanced equipment and technology, reduce labor costs, and transform towards efficient, refined, and intelligent development [30].

4.3. Analysis of Digital Literacy of Tea Farmers

The survey questionnaire on the current status of digital literacy among tea farmers in this study has 7 primary dimensions and a total of 42 objective items. The questionnaire uses the Likert 5-point scale to score the items, with 1 point for strongly disagree, 2 points for strongly disagree, 3 points for generally agree, 4 points for strongly agree, and 5 points for strongly agree. According to the scoring of the Likert 5-point scale, the current level of digital literacy among tea farmers is classified into levels. A score below 3 indicates a relatively low level of digital literacy among tea farmers, a score in the 3–4-point range indicates a moderate level of digital literacy among tea farmers, and a score in the 4–5-point range indicates a high level of digital literacy among tea farmers. Through statistical analysis of the data collected from the survey questionnaire on the current status of digital literacy among tea farmers, the following results were obtained. The situation of the seven major digital literacy of tea farmers is shown in

Table 6. The situation of the seven major digital literacy of tea farmers.

Objective Question Items	Strongly Disagree (%)	Disagree (%)	Generally Agree (%)	Agree (%)	Strongly Agree (%)	Mean Value
A1	0.2	31.8	54.8	12.5	0.7	2.82
A2	0.0	0.2	43.2	39.8	16.8	3.73
A3	2.5	26.8	48.0	22.0	0.7	2.92
A4	0.0	5.0	69.3	24.5	1.1	3.22
A5	0.0	19.5	67.5	12.3	0.7	2.94
A6	0.0	9.8	70.7	17.0	2.5	3.12
B1	0.0	1.6	43.2	47.0	8.2	3.62
B2	0.2	10.9	56.8	29.3	2.7	3.23
B3	0.7	15.0	60.5	21.4	2.5	3.1
B4	0.0	0.5	60.9	31.1	7.5	3.46
C1	0.0	0.0	33.4	57.3	9.3	3.76
C2	0.0	2.3	68.6	28.0	1.1	3.28
C3	1.1	13.0	66.6	18.4	0.9	3.05
C4	0.2	6.6	72.5	19.3	1.4	3.15
C5	0.0	2.7	55.5	30.9	10.9	3.5
C6	0.0	0.0	8.6	63.2	28.2	4.2
D1	0.0	1.4	41.4	51.6	5.7	3.62
D2	10.7	44.5	36.6	7.7	0.5	2.43
D3	9.8	51.6	30.7	5.9	2.0	2.39
D4	0.0	0.7	58.2	31.1	10.0	3.5
D5	0.7	11.8	55.7	26.1	5.7	3.24
E1	0.0	0.0	34.3	50.0	15.7	3.81
E2	0.0	1.6	55.0	40.9	2.5	3.44
E3	0.0	0.9	60.5	36.4	2.3	3.4
E4	0.0	0.0	6.8	64.1	29.1	4.22
E5	0.0	0.7	63.6	31.4	4.3	3.39
E6	0.0	0.2	62.5	34.8	2.5	3.4
F1	0.5	8.0	51.4	37.3	3.0	3.34
F2	3.4	24.3	48.0	23.0	1.4	2.95
F3	0.2	14.3	65.2	16.6	3.6	3.09
F4	0.7	14.1	64.8	17.5	3.0	3.08
F5	0.0	9.8	50.7	31.8	7.7	3.37
G1	0.0	13.2	59.5	26.1	1.1	3.15
G2	0.5	13.6	60.9	23.9	1.1	3.12
G3	0.2	14.8	46.8	32.5	5.7	3.29
G4	19.1	52.3	26.8	1.8	0.0	2.11
G5	3.9	34.3	57.7	4.1	0.0	2.62
G6	3.4	45.5	49.8	1.4	0.0	2.49
G7	51.6	46.8	1.6	0.0	0.0	1.5
G8	4.5	28.9	51.1	14.5	0.9	2.78
G9	2.5	65.5	29.5	2.5	0.0	2.32
G10	0.2	14.8	61.6	18.4	5.0	3.13

(A–G sequentially represent “equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, problem-solving literacy, and professional digital literacy”).

and Figure 1.

4.3.1. Tea Farmers’ Equipment and Software Operation Skills

Tea farmers exhibit significant disparities in “equipment and software operation skills”, with digital technology cognition scoring the lowest average (2.82 points), indicating a deficient level of digital technology understanding. Proficiency in basic computer functions averages 2.92 points, revealing inadequate operational competency. For computer and mobile application software usage, the mean score is 2.94, with 67.5% of respondents rating their proficiency as “average” and noting that software functionality requires enhancement. Scores for digital device connection/interaction, operation interface recognition, and general smartphone function usage fall within the 3–4-point range, with smartphone usage achieving the highest mark. This demonstrates that tea farmers possess stronger smartphone capabilities than computer skills, enabling them to comprehend operation interfaces and execute device connection/interaction tasks—indicating moderate proficiency across these three competencies. The observed variations in device and software operation skills stem from three primary factors: educational foundation, device characteristics, and intergenerational cognition. At the educational level, while 37% of respondents with high school/vocational education can perform basic operations, only 11.6% have participated in training programs, which predominantly focus on operational procedures rather than strategic application, creating a paradox of “knowing how to operate but not how to apply.” Regarding device characteristics, smartphones have emerged as the primary digital tool due to their portability, intuitive interface, and high-frequency usage in scenarios like instant messaging and mobile payments. Consequently, smartphone operational competence (3–4 points) significantly surpasses that of computers. However, computer-related weaknesses persist due to operational complexity, limited usage contexts, and insufficient training, as reflected in low scores for general computer functions (2.92), software applications (2.94), and digital technology cognition (2.82). This underscores the need for a skills development pathway prioritizing “scene-adapted tools first, complex equipment operation to fill gaps, and systematic cognitive enhancement” to transition from “knowing how to use mobile phones” to “effectively leveraging digital tools.” Intergenerationally, the 36–45 age group faces conflicts between rigid experience and technological iteration, while those aged 56+ may exhibit natural aversion to computers due to physical decline and cognitive aging, potentially hindering tangible benefits in tea garden management and sales.

4.3.2. Tea Farmers’ Digital Information Literacy

The average scores for the four dimensions of “tea farmers’ digital information literacy” all fall within the 3–4-point range, indicating a moderate level of digital information literacy among this population. Tea farmers demonstrate the ability to browse, search, and download desired information using mobile phones or computers, while also showing preliminary capacity to discern information authenticity. Mobile devices, computers, and the internet have become essential tools and information sources for tea farmers. This phenomenon fundamentally results from the synergistic effect of digital technology popularization and agricultural production demands. The low-barrier access to smartphones, computers, and networks has reduced physical constraints, enabling tea farmers to perform basic operations—such as information browsing, searching, downloading, and preliminary authenticity verification—while meeting critical production needs like weather monitoring, market price tracking, and agricultural technique acquisition. However, limitations in education levels, training resources, and economic conditions constrain tea farmers’ capacity for in-depth information analysis, precise application, and advanced digital skills (e.g., data modeling, precision marketing). This creates a “tool-dependent but capability-limited” stage characteristic: while their literacy level supports fundamental production activities, it struggles to transcend the “moderate” bottleneck.

4.3.3. Tea Farmers’ Digital Communication and Collaboration Literacy

The survey questionnaire’s “Digital Communication and Collaboration Literacy” module for tea farmers comprised six items assessing communication, interaction, and collaboration in digital environments. The average score for civilized online communication reached 4.2 points (4–5 range), indicating strong adherence to digital etiquette norms. The remaining five dimensions all scored above 3 points (3–4 range), with digital platform usage for tea-related topic communication achieving the highest mark. This distribution reflects tea farmers’ progressive digital competency development—from norm compliance to functional application. The high score in online communication civility (4.2) demonstrates “rule internalization,” stemming from explicit requirements for polite language and reinforced group consensus in digital social contexts. Scores in the 3–4 range across other dimensions indicate a transitional phase from tool proficiency to collaborative efficiency. The prominence of tea-topic communication on digital platforms exemplifies this progression. It aligns with tea farmers’ inherent social nature centered on tea as a medium, while high-frequency topic interactions (e.g., production/sales experience sharing, technical discussions) drive mastery of collaborative skills like commenting, sharing, and work coordination. This “scenario-driven” literacy development pathway ensures civil communication norms while advancing collaboration from “usable” to “effective use” through vertical cultivation of tea-related topics, ultimately forming a digital literacy profile characterized by civilized communication as the surface and collaborative application as the core.

4.3.4. Tea Farmers’ Digital Content Creation Literacy

Tea farmers’ digital content creation literacy focuses on their capacity to create/integrate digital content on platforms, comply with copyright norms, and effectively disseminate information. The survey questionnaire comprises five items, with tea farmers scoring an average of >3 points (3–4 range) in creating, publishing, and sharing tea-related digital content, as well as adhering to digital copyright requirements. This indicates their ability to perform basic content creation/publishing tasks and comply with copyright standards. Conversely, scores for digital content re-editing/integration and tea live streaming fall within the 2–3-point range, revealing deficiencies in advanced content integration skills and adaptability to emerging live streaming formats, hindering their ability to conduct tea live streaming via digital platforms. Tea farmers score high (3–4 points) in digital content creation, publishing and sharing, and copyright compliance, reflecting the ease of use of basic operations (such as simple posting and graphic editing) and platform rules (such as copyright tips), which enable them to quickly master low threshold skills; The low scores (2–3 points) for content re editing and integration (requiring advanced skills such as video editing and multi material fusion) and tea live streaming (involving equipment operation, interactive skills, content planning, and other composite abilities) are due to the general lack of systematic digital skills training among tea farmers, unfamiliarity with advanced tools (such as professional editing software), real-time interactive pressure in live streaming scenes, and cognitive lag in the transformation of traditional production and sales models to new live streaming formats, ultimately forming a hierarchical ability gap of “basic operations can be mastered, complex integration is difficult to control, and live streaming formats are not adapted”.

4.3.5. Tea Farmers’ Digital Security Literacy

The “Digital Security Literacy” dimension in the survey questionnaire evaluates tea farmers’ risk prevention and protection awareness in digital environments, encompassing safe digital infrastructure usage, personal data protection, privacy safeguarding, property security, and related aspects. There are 6 questions in this dimension, and the average score for each question exceeds 3 points. The average score for online fund security is the highest, at 4.22, indicating that tea farmers can effectively protect their property and transfer funds with caution; The average score for security risks in the digital network environment is 3.81, indicating that tea farmers have a basic understanding of the relevant risks; The average score for installing protective software, protecting personal privacy, and preventing online bullying and fraud is about 3.4, indicating that tea farmers have basic security awareness in the online environment and can take preventive measures. The logic behind the difference in scores for the “digital security literacy” dimension among tea farmers essentially reflects their priority and cognitive level of risk prevention in the digital environment: online fund security is directly related to property security, and tea farmers form a strong sense of protection through practical experience (such as cautious transfer), resulting in the highest score (4.22); As a fundamental cognitive level, tea farmers have developed basic risk identification abilities through daily use of digital network environment risks (3.81); On the operational level of installing protective software, protecting privacy, and preventing online bullying and fraud (about 3.4), although possessing basic security awareness, limited by proficiency in using digital tools, privacy protection technology thresholds, or the speed of updating fraud methods, the implementation of preventive measures is not deep enough, forming a gradient difference of “awareness cognition action”, ultimately reflected in the score gap of various dimensions of digital security literacy.

4.3.6. Tea Farmers’ Problem-Solving Literacy

The “problem-solving literacy” of tea farmers focuses on their ability to use digital tools and equipment and solve digital technology problems, including problem identification, evaluation, solution exploration, as well as reflection and updating of their own digital skills. There are 5 items in this dimension, and survey statistics show that tea farmers have an average rating of 2.95 for the difficulty of repairing digital equipment faults, indicating that they still find it difficult to accurately evaluate the difficulty of maintenance. The average score for solving basic problems in the use of digital products is 3.34, which falls within the 3–4-point range, indicating the ability to explore and preliminarily solve operational and usage issues. The average scores for identifying problems and exploring solutions are both close to 3 points, indicating that when facing faults, one will use their mobile phone or computer to explore problems and solutions. The average score for self reflection and improvement in digital skills is 3.37, indicating the ability to objectively evaluate one’s own digital skills and improve weaknesses. In the scenario of using digital tools, tea farmers may find it difficult to accurately evaluate the difficulty of equipment maintenance due to skill limitations. However, they can actively explore problem identification and solutions through digital devices such as mobile phones/computers, and rely on basic operational abilities to preliminarily solve usage problems. At the same time, through self reflection, a virtuous cycle of “practice reflection improvement” is formed. This ability development path from passive response to active exploration, from skill application to self-renewal not only reflects the adaptability strategy of tea farmers to technical problems in the digital age, but also reveals the two major shortcomings that need to be broken through in the improvement in their digital literacy: “precision evaluation ability” and “system problem-solving ability”.

4.3.7. “Professional Digital Literacy” of Tea Farmers

The dimension of “professional digital literacy” for tea farmers focuses on their ability to identify, use digital tools and equipment in the tea industry, and apply digital technology empowerment. Based on the characteristics of tea farmers and their work, as well as the intelligent development of the tea industry, this dimension consists of 10 items. The questionnaire survey shows that there is a significant difference in the “professional digital literacy” of tea farmers, with the highest score of 3.15 in the ability to search for tea work information using mobile phones or computers. The use of fertilizer water intelligent irrigation system to manage tea gardens scored the lowest, with an average of 1.5 points. According to the classification characteristics of the Likert 5-point scale, the average scores for information search, problem solving exploration, tea knowledge learning, and online sales are 3.12–3.29 points, indicating that tea farmers can preliminarily solve tea garden problems and carry out online sales. The average score for intelligent monitoring of tea gardens, crop protection drones, intelligent harvesting, security and anti-counterfeiting traceability, intelligent processing, etc., is 2–3 points, indicating that tea farmers have weak digital skills, limited understanding and exposure to smart agriculture technology. The average score for the application of the fertilizer water intelligent irrigation system is 1–2 points, indicating that tea farmers are basically unfamiliar with and have not been exposed to the system. The logic behind the differences in professional digital literacy among tea farmers is reflected in a progressive impact chain of “tool accessibility skill training application scenarios”: high scores in basic abilities such as information search are due to the popularity of mobile phones/computers, which have lowered operational barriers and are highly compatible with the direct needs of tea farmers for daily production information acquisition and online sales; However, the low scores of smart agriculture technologies such as intelligent monitoring, crop protection drones, and intelligent irrigation for fertilizers and water are due to the higher technical barriers, equipment costs, and professional training required for these technologies. Currently, there are problems in the intelligent process of the tea industry, such as “technology promotion lagging behind equipment research and development”, “training system not covering grassroots tea farmers”, and “application scenarios being disconnected from the actual production needs of tea farmers”. This has led to tea farmers having “little exposure, shallow understanding, and inability to use” complex digital tools, ultimately forming a differentiated pattern of “preliminary mastery of basic digital skills and weak advanced smart agriculture technologies”.

4.3.8. Overall Level of Digital Literacy Among Tea Farmers

By organizing and analyzing the data from the survey questionnaire on the current status of tea farmers’ digital literacy, the overall average score of tea farmers’ digital literacy is 3.21. According to the Likert 5-point scale scoring criteria, the overall level of tea farmers’ digital literacy is at a moderate level.

Table 7. Descriptive statistics on the overall level of digital literacy of tea farmers.

Dimensions	Mean Value	Standard Deviation	Number of People Above Average	Percentage	Number of People Below the Average	Percentage
Equipment and software operation skills	3.13	0.529	230	52.27%	210	47.73%
Digital Information Literacy	3.35	0.542	192	43.64%	248	56.36%
Digital Communication and Collaboration Literacy	3.49	0.432	241	54.77%	199	45.23%
Digital Content Creation Literacy	3.04	0.568	189	42.95%	251	57.05%
Digital Security Literacy	3.61	0.386	193	43.86%	247	56.14%
Problem-solving literacy	3.17	0.591	250	56.82%	190	43.18%
Professional digital literacy	2.65	0.478	209	47.50%	231	52.50%

(Figure 2) is a summary table of descriptive statistics on the overall level of digital literacy among tea farmers.

The survey results indicate that among the seven primary dimensions covered by tea farmers’ digital literacy, the average scores of six dimensions, namely equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, and problem-solving literacy, are in the 3–4-point range, indicating that tea farmers have a moderate level in these six aspects. Conversely, professional digital literacy averages below 3 points, signaling a relatively low competency level and weak adaptability to the tea industry’s digitalization and intelligent transformation. From the mean values of the seven dimensions and the proportion of people above and below the mean, the difference in the number of people above and below the mean is not significant. Ranked in descending order of average score, they are digital security literacy, digital communication and collaboration literacy, digital information literacy, problem-solving literacy, equipment and software operation skills, digital content creation literacy, and professional digital literacy. Overall, the digital literacy level of tea farmers urgently needs to be improved, especially their professional digital literacy. The digital literacy of tea farmers presents a hierarchical feature of “moderate basic skills and weak professional application”. The underlying logic is that basic digital skills such as equipment operation and information acquisition have been accumulated through daily use (3–4-point range), while professional digital literacy, as an ability that needs to be deeply applied in combination with the characteristics of the tea industry (such as digital production management, intelligent device collaboration, data-driven decision-making, etc.), lacks targeted training, industry scenario adaptation tools, and practical opportunities, resulting in an average score of less than 3 points; Digital security literacy is highly valued and scored the highest due to its involvement in vital interests such as fraud prevention and account protection; The proportion of high/low scoring individuals in the mean distribution of each dimension is close, reflecting individual differences in digital literacy among tea farmers in various dimensions, but no significant ability gap has been formed as a whole. This highlights the need to prioritize the precise improvement in professional digital literacy through a “career scenario oriented” training system (such as intelligent tea picking equipment operation, e-commerce live streaming skills, data traceability system application, etc.), in order to break through the bottleneck of “using tools but not using digital to empower production” in the digitalization process of the tea industry, and ultimately achieve a leap in ability from basic skills to professional applications.

Results from the Two-Tailed correlation test in

Table 8. Two- Tailed Test.

		A	B	C	D	E	F	G
A	Pearson correlation	1	0.831 **	0.851 **	0.780 **	0.694 **	0.813 **	0.798 **
	Sig. (Two-Tailed Test)		0.000	0.000	0.000	0.000	0.000	0.000
	Number of cases	440	440	440	440	440	440	440
B	Pearson correlation	0.831 **	1	0.795 **	0.777 **	0.717 **	0.785 **	0.783 **
	Sig. (Two-Tailed Test)	0.000		0.000	0.000	0.000	0.000	0.000
	Number of cases	440	440	440	440	440	440	440
C	Pearson correlation	0.851 **	0.795 **	1	0.790 **	0.729 **	0.790 **	0.799 **
	Sig. (Two-Tailed Test)	0.000	0.000		0.000	0.000	0.000	0.000
	Number of cases	440	440	440	440	440	440	440
D	Pearson correlation	0.780 **	0.777 **	0.790 **	1	0.740 **	0.732 **	0.788 **
	Sig. (Two-Tailed Test)	0.000	0.000	0.000		0.000	0.000	0.000
	Number of cases	440	440	440	440	440	440	440
E	Pearson correlation	0.694 **	0.717 **	0.729 **	0.740 **	1	0.703 **	0.732 **
	Sig. (Two-Tailed Test)	0.000	0.000	0.000	0.000		0.000	0.000
	Number of cases	440	440	440	440	440	440	440
F	Pearson correlation	0.813 **	0.785 **	0.790 **	0.732 **	0.703 **	1	0.889 **
	Sig. (Two-Tailed Test)	0.000	0.000	0.000	0.000	0.000		0.000
	Number of cases	440	440	440	440	440	440	440
G	Pearson correlation	0.798 **	0.783 **	0.799 **	0.788 **	0.732 **	0.889 **	1
	Sig. (Two-Tailed Test)	0.000	0.000	0.000	0.000	0.000	0.000
	Number of cases	440	440	440	440	440	440	440

** At the 0.01 level (Two-Tailed Test), the correlation is significant (A–G sequentially represent “equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, problem-solving literacy, and professional digital literacy”).

demonstrate a statistically significant correlation (p = 0.000) across all seven dimensions of digital literacy among medium-sized tea farmers (n = 440) in Anxi County. This systemic correlation indicates that digital literacy dimensions do not develop in isolation but exhibit significant interdependent characteristics—within the context of the tea industry’s intelligent transformation, these dimensions form a dynamically interconnected system. Specifically, this correlation is reflected in the practical logic of the technology and knowledge spillover era. When a certain literacy (such as device operation skills) is improved, it often promotes the synchronous improvement in other literacy (such as digital information literacy or collaborative communication skills) through mechanisms such as knowledge transfer, tool sharing, or collaborative feedback. For example, the correlation between device and software operation skills and digital communication and collaboration skills directly supports the closed-loop practice of “device operation data interpretation team collaboration”; The correlation between professional digital literacy and problem-solving ability reflects the deep correlation between intelligent device data integration and planting decision optimization. However, while significant correlations exist, they do not imply strict causal chains—there is no predetermined sequence where one dimension must be prioritized before others follow. Improvements manifest more as a “collaborative” relationship than a unidirectional causal pathway. This characteristic challenges traditional dimensional difference analysis, as actual development resembles “touchpoint” mutual reinforcement and holistic advancement. Consequently, digital literacy cultivation strategies should prioritize constructing a multi-dimensional collaborative support system over linear, single-dimension promotion. Such an approach aligns with the interconnected nature of digital competencies and the industry’s intelligent transformation demands, fostering systemic rather than fragmented skill development.

5. Discussion

5.1. Enhancing the Digital Literacy of Tea Farmers Requires Multidimensional Efforts

The improvement in tea farmers’ digital literacy requires the construction of a three-dimensional collaborative system of “Government-Industry association-Training institution” and the realization of a full chain leap through multidimensional efforts. In terms of equipment and software operation skills, the government can prioritize “scenario adaptation tools”, popularize basic knowledge of intelligent device operation, promote research and development subsidies for aging friendly intelligent devices, and provide subsidies for the rental of 5G networks and intelligent terminals. Through a combination of online and offline promotional materials, tea farmers of all ages can be covered, such as step-by-step operation manuals and video tutorials, with a focus on breaking through the pain points of using complex computer functions. Industry associations can encourage technological innovation and equipment upgrades, develop aging friendly operating interfaces, and establish cross generational mutual aid communities to achieve skill sharing and resource platform co construction. Training institutions can conduct “modular training + practical workshops”, design “advanced courses for smart devices” for the 36–45 age group and offer “voice operation training courses” for the 56 years old and above age group. In terms of digital information literacy, the government should improve the popularization of 5G base stations and intelligent terminals in rural areas, establish typical cases through the demonstration of “digital tea gardens”, and form a virtuous cycle of “learning and application integration”. Industry associations can establish “industry certifications” to incentivize tea farmers to enhance their advanced skills, strengthen their specialized abilities in digital information acquisition, application, and precision marketing. The training machine can build a progressive training system of “basic skills—advanced applications”. In terms of digital communication and collaboration literacy, the government can jointly formulate the “Guidelines for the Use of Tea Industry Digital Collaboration Platforms”, promote the sharing of production and sales experience system, and cultivate a “problem oriented” collaboration awareness. Industry associations promote specialized collaboration platforms for the tea industry, organize in-depth interactions on tea topics to enhance collaboration efficiency, such as technical seminars and production and sales docking. Training institutions should popularize digital communication norms, such as civilized etiquette in online communication, during training. In terms of digital content creation literacy, the government can organize activities such as “Tea Farmer Creation Competition” and “Marketing Strategy Workshop” to cultivate a full chain marketing thinking of “content traffic conversion”. Industry associations should stimulate the creative enthusiasm of tea farmers and create a good atmosphere. Training institutions should strengthen basic training such as graphic editing and short video production. In terms of digital security literacy, the government can carry out hierarchical propaganda and education on “fund security—privacy protection—fraud prevention”, improve the dual mechanism of “technical protection + institutional guarantee”, and enhance the risk identification awareness of tea farmers. Industry associations can take the lead in establishing the “Tea Farmers Digital Security Alliance”, regularly issuing regional fraud case warnings, and coordinating member units to share privacy protection technologies. Training institutions should strengthen the application of protective software installation, privacy settings, and other operation guidelines by tea farmers. In terms of problem-solving literacy, the government can establish a dual platform of “remote service + local support” to provide remote services such as equipment fault diagnosis and technical problem consultation. Industry associations can expand their solution library through tea farmer experience sharing sessions and cross-border technical cooperation, such as agricultural experts + IT engineers. Training institutions can focus on the diverse needs of tea farmers and provide targeted education to enhance their ability to solve systematic problems. In terms of professional digital literacy, the government should take “digital thinking leading application innovation” as the goal, build a “learning platform + practice base” resource network, and promote tea farmers to explore digital innovation applications in tea garden management, sales and other aspects. Industry associations should jointly develop professional digital literacy standards, promote the certification system for “digital tea farmers”, and facilitate the inheritance of experience and skill iteration. Training institutions should develop professional scenario courses such as intelligent tea picking and e-commerce live streaming and achieve experience inheritance through the “tea farmer mentor system”.

5.2. Analysis of Digital Literacy of Tea Farmers: Current Status, Relationships, and Improvement Strategies

In the research on the correlation between digital literacy of tea farmers and the development of smart agriculture, tea farmers who operate on a moderate scale generally have a moderate level of digital literacy, with the lowest level being professional digital literacy. This is in line with the demand for high skills among tea farmers in smart agriculture. Tea farmers are at a moderate level in all six dimensions of digital literacy. Although they have certain technical application abilities, there is still room for improvement. Low professional digital literacy will affect the efficiency of smart agricultural equipment and the efficient and sustainable development of the tea industry. At the same time, the various dimensions of digital literacy are interrelated and influenced. Equipment and software operation skills are the foundation, while digital security literacy is the guarantee. In this study, tea farmers scored high in digital security literacy and had strong risk prevention awareness, but scored low in digital content creation literacy, which affected brand promotion, product marketing, and market competitiveness. By drawing on the United Nations Digital Literacy Framework to construct a scale to measure the current status of tea farmers’ digital literacy, targeted measures such as popularizing knowledge, providing training and practice opportunities, encouraging innovation and upgrading, establishing mutual aid communities and resource sharing platforms have been proposed. These measures are effective and feasible. Multi-channel popularization of knowledge can help tea farmers master basic operations, providing training and practice can improve their skills and application abilities, encouraging innovation and upgrading can promote technological progress, and establishing platforms can promote communication and cooperation, reduce costs, and enhance literacy.

5.3. Digital Literacy Enhancement for Tea Farmers: A Catalyst for Holistic Agricultural Advancement in Production, Efficiency, Market Access, and Resilience

The enhancement of digital literacy among tea farmers serves as a pivotal catalyst for comprehensive agricultural advancement, manifesting in four interconnected dimensions: productivity optimization, operational efficiency, market accessibility, and risk resilience. At the production level, digitally empowered farmers gain proficiency in precision data acquisition and analytical capabilities. Through the deployment of smart sensing devices, they achieve real-time monitoring of critical parameters such as microclimate temperature-humidity dynamics and soil nutrient profiles within tea gardens. This enables scientifically informed irrigation and fertilization strategies, yielding significantly higher yields and enhanced product quality competitiveness compared to traditional methods. In terms of operational efficiency, farmers with advanced digital skills demonstrate mastery in leveraging agricultural IoT platforms to orchestrate end-to-end production workflows. This digitized coordination enhances synergy across harvesting, processing, and packaging stages, culminating in elevated collaborative efficiency and reduced labor costs. A notable application emerges in pest and disease surveillance, where mobile-enabled real-time pest monitoring systems empower farmers to initiate proactive prevention protocols, thereby minimizing pesticide usage while ensuring product safety and environmental sustainability. Regarding market access, digitally literate producers capitalize on e-commerce competencies to establish direct-to-consumer channels. By showcasing artisanal tea-processing techniques via short-video platforms, they circumvent traditional wholesale price barriers, driving online order growth and premium pricing opportunities. This digital market penetration strategy fundamentally transforms value chain dynamics. Crucially, digital literacy fosters a data-driven decision-making paradigm among tea cultivators. This cognitive transformation equips them with superior risk mitigation capabilities when confronting climatic volatility, market price fluctuations, and other uncertainties. Through systematic integration of digital tools and analytical frameworks, farmers achieve simultaneous enhancement in production performance, operational efficiency, market competitiveness, and adaptive resilience—collectively constituting a paradigm shift in sustainable tea agriculture.

It is also meaningful to contextualize the case of enhancing tea farmers’ digital literacy in this study within the broader framework of agricultural modernization through cross-regional and cross-crop comparative analysis. For instance, in the fruit cultivation sector, the Netherlands has achieved a 40% increase in yield and a 30% improvement in water resource utilization through digital greenhouse technology enabling precise environmental control. Meanwhile, Kenyan coffee farmers adopting blockchain traceability systems have expanded their product premium space by 25%, directly aligning with international premium market standards. Through such cross-regional and cross-crop comparative analyses, it becomes evident that the enabling logic of digital literacy for agricultural stakeholders is highly transferable—whether in temperate fruit and vegetable production, tropical cash crops, or tea cultivation, the core lies in data-driven optimization of production factors. Such cross-sector and cross-territorial comparative research not only validates the universality of digital literacy enhancement pathways but also reveals differentiated characteristics and synergistic effects in technology adoption across diverse agricultural ecosystems. Therefore, this study can provide some reference for countries undergoing agricultural digital transformation.

5.4. Research Limitations and Future Research Directions

Previous studies on digital literacy have primarily centered on evaluation frameworks, particularly targeting students, teachers, and other populations. Systematic research on tea farmers remains scarce, with methodologies predominantly relying on questionnaire surveys, interviews, and case analyses that emphasize challenges and strategies for enhancing farmers’ digital literacy. This study, however, focuses on moderate-scale tea farming operations, drawing upon the United Nations Digital Literacy Framework and integrating the unique characteristics of the tea industry and its farmers to develop an innovative evaluation framework. By doing so, it not only enhances the framework’s applicability to diverse research groups in developing countries but also enriches the academic discourse on measuring farmers’ digital literacy, offering practical implications for the intelligent transformation of the tea industry. Nevertheless, this study acknowledges several limitations: restricted generalizability due to the sample being exclusively drawn from Anxi County, reliance on a single research methodology, and the need to deepen the alignment between tea farmers’ actual behaviors and the specificities of tea production. Future research should expand sample sizes, employ mixed-methods approaches, design intervention experiments to validate proposed strategies, and investigate variations in digital literacy across different types of tea farmers. Additionally, it should explore how improvements in digital literacy influence various benefits of the tea industry and how digital technologies shape the requirements for digital literacy across multiple dimensions.

6. Conclusions

6.1. High Reliability of Questionnaire

Through reliability testing of the questionnaire data on tea farmers’ digital literacy levels, Cronbach’s alpha coefficient demonstrated strong overall and dimensional reliability (indicating internal consistency across items and dimensions). Parallel validity analyses via KMO and Bartlett’s sphericity tests further confirmed the questionnaire’s robust structural validity, with excellent inter-item correlations and discriminant validity among all measurement items.

6.2. Basic Information of Tea Farmers

In the survey sample, tea farmers exhibit a balanced gender distribution and are predominantly aged 26–45 years, with educational attainment concentrated at junior high school, high school, or vocational school levels. Most have joined tea cooperatives and participate in farmer training programs, with the majority engaged in tea cultivation for 6–15 years. Notably, respondents frequently manage contiguous tea gardens spanning 20–30 acres and use electronic devices for 2–4 h daily. Their understanding of smart agriculture is moderate, yet most recognize the developmental trajectory of smart agricultural equipment and technology. They express aspirations to introduce these innovations across the entire tea industry value chain.

6.3. Digital Literacy Level

Among the seven primary dimensions of tea farmers’ digital literacy, six dimensions—equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, and problem-solving literacy—exhibit average scores in the 3–4-point range, indicating a moderate proficiency level. Conversely, professional digital literacy scores below 3 points, signaling a relatively low competency level. This disparity underscores an urgent need for comprehensive digital literacy enhancement, with professional digital literacy emerging as the most critical gap requiring targeted intervention.

6.4. Build a Cultivation System of “Multi-Dimensional Collaboration, Scenario Driven, and Dynamic Feedback”

For training design, avoid linear progression in a single dimension and instead promote knowledge transfer and skill linkage through cross dimensional integration scenarios of “device operation information analysis collaborative communication problem solving” (such as intelligent device data integration and live sales collaborative practice). In implementation strategies, establish a “touchpoint” stimulation mechanism, trigger multi literacy synchronous improvement through specific tasks such as intelligent monitoring of tea gardens and online sales data analysis, while supporting dynamic feedback systems to track the balance of development in various dimensions. Regarding support and guarantee, we will integrate government, enterprise, and research institution resources, provide layered training from basic operations to professional applications, and strengthen infrastructure support such as intelligent device sharing and collaboration tool popularization, ultimately achieving overall improvement in digital literacy rather than local breakthroughs, and helping tea farmers better adapt to the intelligent transformation needs of the tea industry.

6.5. Basis for Improvement Strategy

Moderate-scale tea farmers in Anxi County possess the necessary conditions to adopt smart agricultural equipment and technologies. Based on the measurement and research of seven aspects of tea farmers’ literacy in Anxi County, this study draws on the countermeasures proposed by previous researchers from the three levels of “government, industry associations, and training institutions” and proposes targeted improvement strategies to provide support for the intelligent transformation of the tea industry.