Available online at www.HighTechJournal.org  

HighTech and Innovation  
Journal 

Vol. 5, No. 2, June, 2024 

 

 

295 

 

ISSN: 2723-9535 

 

Social Media, Knowledge Management, and Learning in Farmer 

Innovation 

Guihua Xie 1, Xiaofeng Su 1* , Meijiao Huang 1 

1 College of Business Administration/Enterprise Innovation and Development Research Center, Fujian Business University, Fuzhou, 350012, China. 

Received 24 December 2023; Revised 11 May 2024; Accepted 17 May 2024; Published 01 June 2024 

Abstract 

This study aims to address the research gap by investigating how social media influences the innovation ability of new 

professional farmers, with a specific focus on technological perspectives. Grounded in embeddedness theory and taking 

into account the roles of knowledge management and learning orientation, the research aims to unveil the dynamics shaping 

farmer innovation within the context of social media engagement. Employing a structural equation model and utilizing 

survey data from 336 farmers, the empirical research concludes that social media embedding significantly and positively 

impacts the innovation ability of new professional farmers. Knowledge management acts as a partial intermediary between 

network media embedding and the innovation ability of new professional farmers, and a complete intermediary between 

network community embedding and their innovation ability. Learning orientation positively moderates the relationship 

between network media embedding, network community embedding, and knowledge management. The study seeks to 

contribute to the comprehension of how social media can foster innovation among farmers, thereby promoting high-quality 

and sustainable development in agriculture. In light of this, recommendations are suggested for the government to 

encourage social media usage, for farmers to enhance their media literacy, strengthen knowledge management, and 

cultivate a learning-oriented mindset. 

Keywords: Social Media Embedding; Network Media Embedding; Network Community Embedding; Farmer Innovation Ability; 

Knowledge Management; Learning Orientation. 

 

1. Introduction 

The agricultural landscape has undergone a transformative evolution in recent years, marked by a discernible shift 

towards high-tech methodologies and innovative practices [1]. In this era of rapid technological advancement, the 

farming industry is increasingly embracing cutting-edge technologies to enhance productivity, efficiency, and 

sustainability. The integration of digital tools, precision agriculture, and data-driven decision-making has ushered in a 

new era of agricultural practices, redefining traditional approaches to farming [2]. As the farming sector continues to 

grapple with challenges posed by a growing global population, climate change, and resource constraints, the adoption of 

innovative solutions becomes imperative [3]. High-tech innovations not only promise increased yields and resource 

optimization but also open up new possibilities for aspiring and emerging professional farmers [4]. Understanding the 

role of social media in fostering innovation among new professional farmers becomes a crucial area of exploration, 

particularly considering the potential intermediaries such as knowledge management and the moderating influence of 

learning orientation [5]. The contribution of new professional farmers to the revitalization of rural industries, economic 

 
* Corresponding author: ifengsu@fjbu.edu.cn 

 
http://dx.doi.org/10.28991/HIJ-2024-05-02-06 

➢ This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). 

© Authors retain all copyrights. 

https://creativecommons.org/licenses/by/4.0/
https://orcid.org/0000-0002-6420-063X


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development, and social prosperity in China is undeniable [6]. At all levels of government in China, there is a strong 

emphasis on providing vocational training for new farmers to enhance their innovation and entrepreneurship capabilities 

[7]. Innovation has always been a vital component of agricultural development, and farmers' innovative ability is no 

exception. Globally, farmers' innovation, such as selecting new agricultural varieties, utilizing new machinery, and 

adopting novel business models, can amplify agricultural production scale, improve food quality, and help farmers 

reduce production costs by enhancing labor efficiency [8]. Farmers' innovation and rational use of resources such as 

water, land, and labor can sustain the rural environment and economic development, aligning with the United Nations 

Sustainable Development Goals to a greater extent [9]. 

Innovation ability is the embodiment of innovative behavior and comprehensive quality, and it is crucial for the 

development of innovative activities and performance [10]. Exploring the formation mechanism of farmers' innovation 

ability is helpful for cultivating their innovative skills [11]. Currently, scholars have primarily investigated the impact 

of government policies [12], non-cognitive skills [13], and peer effects [14] on farmers' innovation ability from 

institutional, individual traits, and network perspectives, respectively. Information technology is a significant force 

driving innovation, and its role is increasingly receiving attention from scholars in the agricultural field. A survey 

conducted among female small farmers in Kenyan agricultural communities found that farmers have a positive attitude 

toward information technology tools, which support their agricultural operations, improve agricultural productivity, and 

provide convenient and accessible markets for their products [15]. More research has also begun to analyze the 

relationship between information technology and agricultural innovation to further the development of emerging digital 

tools. However, through reading relevant literature, it can be found that current research mainly focuses on the role of 

traditional information technology tools such as television, the internet, and radio [16], and there is relatively little 

analysis of social media as the latest information tool, especially a lack of specific path research on the role of social 

media in farmers' innovation. In rural China, new professional farmers have used WeChat, TikTok, and other social 

media to carry out innovative agricultural management and have achieved remarkable results [17]. Therefore, exploring 

the relationship between the latest social media tools and farmers' innovation is particularly urgent. 

In addition, network media embedding is described as the integration of social media platforms into farmers' 

communication networks, facilitating knowledge exchange and information sharing within agricultural communities 

[18]. This exchange of information fosters idea generation and problem-solving, thereby enhancing innovation abilities 

among farmers [19]. Similarly, network community embedding, characterized by farmers' involvement in online 

agricultural communities and forums, has been shown to positively impact innovation ability [20]. The role of online 

communities in providing a supportive environment for knowledge acquisition, collaboration, and collective learning is 

essential. Through active participation in these communities, farmers gain access to diverse expertise, resources, and 

innovative ideas, contributing to the development and implementation of novel farming practices and technologies [21]. 

Hence, this research aims to examine the relationships of network media embedding and network community embedding 

on farmers’ ability to innovate. 

The development of agricultural systems is based on various innovative processes, which are currently being 

challenged by sustainability issues. Especially today, various reports and scientific research emphasize the knowledge 

of farmers as a way to design more sustainable agricultural systems [22]. Knowledge and Innovation Management (KIM) 

can play a key role in cultivating and managing the creativity of integrated enterprises [23]. Information technology is 

widely used for knowledge management [24]. In many countries around the world, farm innovators regularly use 

WhatsApp on their phones. They use social media to learn and share personal agricultural management knowledge and 

experience, build knowledge through social networks, innovate farmer learning and exchange forums, and promote the 

creation of social wealth [25]. In Mexico, female farmers use digital tools such as mobile phones and the internet to 

manage the knowledge and technology contained in the traditional Tequila Mescal process, reflecting the significance 

of knowledge management for sustainable agricultural development [26]. In China's rural areas, new professional 

farmers extensively employ social media platforms like WeChat and TikTok to sell agricultural products and carry out 

agricultural operations, resulting in a continuous stream of innovation activities [27]. Farmers have also made significant 

improvements in their knowledge of markets, products, and marketing, but there is relatively little research on social 

media, knowledge management, and the relationship between their outcomes. The practicality of farmers' knowledge 

management is far ahead of theoretical research. 

Research has elucidated that network media embedding, involving the integration of social media platforms into 

farmers' communication networks, facilitates the dissemination and sharing of agricultural knowledge and information 

[18]. Utilizing online platforms, farmers gain access to a diverse range of agricultural resources, expert insights, and best 

practices, thereby enriching their knowledge base and improving knowledge acquisition processes [28]. Similarly, 

network community embedding, characterized by farmers' participation in online agricultural communities and forums, 

contributes to effective knowledge management [20]. Research has emphasized the role of online communities in 

fostering collaborative knowledge creation, exchange, and retention among farmers. Through active participation in 

discussions, sharing experiences, and seeking advice within these communities, farmers can tap into collective 

intelligence, leveraging diverse perspectives and expertise to address agricultural challenges and enhance farming 

practices [29]. Hence, this research aims to explore the relationships of network media embedding and network 

community embedding in knowledge management.  



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Knowledge management plays a critical role in fostering innovation ability among farmers. By systematically 

organizing and leveraging existing knowledge resources, farmers can enhance their problem-solving abilities and 

generate novel ideas to improve agricultural practices [30]. Furthermore, knowledge management facilitates the creation 

of conducive environments for innovation. The importance of knowledge sharing and collaboration in stimulating 

innovation among agricultural communities is essential. Through knowledge management initiatives, such as training 

programs, workshops, and knowledge exchange platforms, farmers can exchange ideas, experiences, and insights, 

fostering a culture of innovation and experimentation [31]. Moreover, knowledge management enables the effective 

utilization of external knowledge and expertise, which is essential for innovation. The role of knowledge sourcing from 

external networks, including research institutions, universities, and industry experts, in driving innovation in agriculture 

[29]. By accessing external knowledge networks and leveraging external resources, farmers can access cutting-edge 

technologies, innovative practices, and market insights, thereby enhancing their innovation ability [28]. As a result, this 

research explores knowledge management as a mediating variable for the relationships of network media embedding 

and network community embedding on the farmer’s ability to innovate. 

In a dynamic environment, achieving sustainable agricultural development demands that farmers possess strong 

adaptability [32], making it necessary for farmers to establish a strategic concept of learning orientation and develop a 

habit of lifelong learning. Studies have long proven that learning orientation has a significant impact on employees' 

knowledge management [33]. Currently, while many farmers use social media, variations exist in the management 

effects and innovation abilities concerning social media information and knowledge among individual farmers [34]. 

Does this difference result from differences in new professional farmers' learning orientation? Can learning orientation 

regulate the relationship between new professional farmers and social media? Can it encourage new professional farmers 

to better use social media tools to obtain better knowledge management results? Currently, there is no relevant research 

analyzing these issues. Hence, this research employs learning orientation as a moderating variable.  

This study addresses several critical research gaps by employing the embeddedness theory. Firstly, it contributes to 

the understanding of social media's role in enhancing innovation ability among new professional farmers, an area where 

empirical research is scarce. Second, by adopting embeddedness theory and considering structures within a particular 

context, this study sheds light on how social media embedding influences innovation dynamics within agricultural 

communities. Additionally, by integrating the perspectives of knowledge management and learning orientation, 

embeddedness theory provides a holistic framework for analyzing the complex interactions between social media use, 

knowledge sharing, learning processes, and innovation outcomes among farmers. Therefore, this study not only advances 

our understanding of social media's impact on farmer innovation but also demonstrates the utility of embeddedness 

theory in addressing multifaceted research inquiries in agricultural contexts. 

This study utilizes embeddedness theory to analyze the impact of various social media embedding methods on the 

innovative ability of new professional farmers from the perspective of knowledge management. It aims to reveal the 

specific path of network embedding and media embedding's influence on innovative ability, explore the mediating effect 

of knowledge management, and investigate the moderating effect of learning orientation. The goal is to provide reference 

and inspiration for cultivating the innovative ability of new professional farmers under information technology scenarios 

and to offer better human resource reserves for agricultural sustainable development. 

2. Theoretical Foundation and Research Hypothesis 

2.1. Social Media Embedding 

Embeddedness theory suggests that organizational economic behavior is closely related to social systems [35]. In the era 

of the internet, internet embedding is used to measure the relationship between human behavior and the internet. 

According to Yongyun et al. [36], the internet serves as both a content platform and a network platform. Therefore, 

internet embedding is represented by two dimensions: network community embedding and media embedding. Social 

media, as a content production and communication platform based on internet-user relationships [37], has become a 

typical manifestation of internet embedding. Social media embedding reflects the extent to which people use social 

media for work and daily life, showcasing the interdependent relationship between people and social media. Similarly, 

social media embedding can be divided into two dimensions: network community embedding and network media 

embedding [36]. Network community embedding pertains to the breadth and closeness of various social relationship 

networks formed by entrepreneurs through the use of social media tools such as instant messaging and short videos on 

the internet. For instance, some African women have expanded their social circles through social media, developed 

personal care and beauty products at home, and grown into female entrepreneurs [38]. Network media embedding refers 

to the frequency and extent of entrepreneurs' use of social media network media functions. This includes obtaining 

information through search and browsing on social media, publishing information through content creation, forwarding, 

and commenting, and conducting social activities through voice and video functions [18]. From the perspective of 

embeddedness, analyzing the impact of different embedding methods of social media on farmers' innovative ability 

contributes to a deeper understanding of the ways in which social media affects farmers' innovative behavior. 



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2.2. Knowledge Management Theory 

According to knowledge management theory, acquiring, integrating, and creating new and valuable knowledge are 

key to maintaining a competitive advantage for enterprises and significantly influence the enterprise’s performance and 

sustainable development [39]. In the field of agriculture, farmers' knowledge management has long been considered an 

important factor in determining the quality of agricultural operations [40]. Agricultural entrepreneurship mostly involves 

small and micro-scale operations, and the comprehensive quality and ability of farmers often determine the success or 

failure of agricultural entrepreneurship. During facing natural disasters, the fragility of agricultural operations becomes 

evident, and farmers' knowledge, values, and other factors influence their adaptive decision-making and behavior, 

resulting in different agricultural operation results [41]. Therefore, strengthening knowledge management for farmers is 

an important way to deal with risks and improve business performance [42]. Knowledge is the main driving force for 

innovation, and the development of sustainable agricultural innovation requires farmers to have various knowledge 

reserves, including adaptive crops, the local environment, and farming systems [43]. Therefore, farmers should not only 

use existing knowledge but also invest in external knowledge acquisition. In France, farmers draw on a variety of 

knowledge sources, exhibiting individual differences in the intensity of knowledge use and determining their differences 

in sustainable agricultural innovation [44]. Strengthening research on the relationship between social media and farmers' 

knowledge management is an effective exploration of information technology methods to improve farmers' knowledge 

management levels. 

2.3. Learning Orientation 

According to Calantone et al. [45], learning orientation is the foundation of learning, with the aim of generating a 

learning process. Learning orientation is often used to develop new insights [46] and create and manage knowledge [47]. 

In practice, learning orientation continuously stimulates organizational learning through openness of thinking and 

commitment to learning. According to Liao et al. [48], organizational learning ability is the source of sustainable 

competitive advantage, significantly affecting organizational knowledgeability and innovation [49]. 

2.4. Social Media Embedding and Innovation Ability 

Organizations' information technology resources can enhance their performance [50]. Social media serves not only 

as a medium for entrepreneurial learning and self-employment but also as a tool for entrepreneurial marketing and a 

source of entrepreneurial opportunities. It also acts as a facilitator of entrepreneurial networks and ecosystems [51], 

playing a crucial role in integrating internal and external resources and developing new products and services [52]. Social 

media allows the publication of videos, and learning courses through video intermediaries are more effective than 

traditional seminars and training. This can improve farmers' understanding of plant pesticides, alter their attitudes, and 

ultimately lead them to adopt these methods. Videos can convey complex issues and explain the biological and physical 

processes underlying agricultural innovation, making it easier for farmers to accept innovation and thereby enhancing 

their innovation ability [53]. Social media also promotes innovative interaction. Scholars widely recognize the use of 

social media to leverage the wisdom of the masses in shaping significant commercial decisions and societal lifestyles 

[54]. Numerous studies have demonstrated that social media can drive innovation in the retail business [55]. In the 

agricultural products retail industry, both radical and incremental innovations have significantly increased with the use 

of social media. A study on Spanish family businesses found that social media has completely transformed the 

relationship between the enterprise and the market. Family businesses can use social media to connect and collaborate 

with different stakeholders, strengthening open innovation activities [56]. In Indonesia, farmers traditionally obtain 

information on agriculture, forestry, and their innovations through interpersonal communication. Social media has built 

a rich social network that facilitates interpersonal communication on the network at a lower cost, faster speed, and in a 

more convenient manner. This is more conducive to the role of farmer experts and opinion leaders in promoting 

innovation dissemination [57]. Based on the above research, the following hypotheses are drawn: 

H1: Network media embedding has a significant positive impact on the innovation ability of new professional 

farmers. 

H2: Network community embedding has a significant positive impact on the innovation ability of new professional 

farmers. 

2.5. Social Media Embedding and Farmer Knowledge Management 

IT technology is one of the key factors affecting knowledge management, helping enterprises improve the efficiency 

of knowledge storage, sharing, and acquisition [58]. Academic research has found that social media is increasingly used 

as a tool to manage the flow of knowledge within and across organizational boundaries in the innovation process [54]. 

As a popular medium, social media also has a significant impact on farmers' agricultural technology and related 

knowledge. Social media serves as a potential communication channel for farmer interaction and knowledge sharing. 

Foreign research scholars have found that Twitter, as an example, can capture the immediacy and visual impact of real-



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time operations. The brief messages shared through Twitter attract time-limited farmers to learn and share agricultural 

innovation practices on social media. Farmers also form virtual networks around specific topics. Within these networks, 

farmer entrepreneurship models emerge and are respected by other farmers. Proactive farmer innovators can learn more 

about agricultural technology and management knowledge from entrepreneurship models and experts in virtual networks 

[59]. A survey of Rwanda and Uganda found that mass media promotional activities such as mobile text messages and 

video screenings are significantly associated with increasing farmers' awareness of pesticide risks and safety prevention 

measures. This can encourage farmers to adopt safer pest management strategies [60]. In foreign countries, online social 

networking technology is also used by agriculturally integrated enterprises to access information and generate 

knowledge. This provides supplementary information for management and decision-making in the agricultural system, 

allowing companies to respond more quickly to market changes [61]. Scholarly research also confirms that farmers using 

smartphones with appropriate applications have a higher adoption rate of innovative fertilization practices than those 

listening to radio broadcasts. They perform better in agronomic knowledge and are more cost-effective. Therefore, 

promoting the use of smartphones among farmers has an important positive effect on the dissemination of agricultural 

information technology [62]. Based on the above research, the following hypotheses are drawn: 

H3: Network media embedding has a significant positive impact on the knowledge management of new professional 

farmers. 

H4: Network community embedding has a significant positive impact on the knowledge management of new 

professional farmers. 

2.6. Knowledge Management and Innovation Ability 

Knowledge management is a decisive factor for enterprise innovation, and through knowledge management, the 

decision-making environment gains the ability to effectively manage stakeholder satisfaction processes [63]. Both 

knowledge acquisition and integration, as well as creation, are positively correlated with dual innovation [64]. Scholarly 

research has found that companies embedded in automotive industrial clusters can enhance their innovation ability by 

strengthening knowledge management, influencing innovation performance [65]. Executives of companies can catalyze 

innovation through knowledge management, positively promoting enterprise innovation [66]. 

Virtual and real practice communities built based on social media also play a crucial role in promoting farmers' 

innovative behavior. Farmers who collectively construct knowledge in practice communities are better equipped to 

innovate than those who work alone with expert support [67]. According to Rosário et al. [68], the ability and behavior 

of farmers' sustainable agricultural innovation are influenced by the relevant knowledge, attitudes, and subjective norms 

of sustainable innovation. By enhancing the management of relevant knowledge, the probability of farmers' sustainable 

agricultural innovation will increase. It is evident that knowledge is a necessary element for the growth of agricultural 

enterprises, and the management ability of farmer knowledge significantly influences the promotion of the innovation 

ability of agricultural enterprises [23]. Based on the above research, the following hypothesis is drawn: 

H5: The knowledge management of new professional farmers has a significant positive impact on innovation ability. 

2.7. Knowledge Management as a Mediating Factor 

Scholars have found in their study of the development of small and medium-sized enterprises in South Asia that 

social media has led to the joint creation of knowledge in developing societies and has thereby promoted innovation , 

accelerating the growth of the creative economy [69]. Chilean fruit farmers use integrated and bridging social capital 

to explore and acquire new knowledge and resources, ultimately promoting the implementation of new agricultural 

technologies and practices. Different participants in farmers' social networks, such as peers, advisors, and researchers, 

provide support for the knowledge needed for agricultural innovation on farms [70]. According to Cepeda-Carrion 

et al. [56], in Spain, social media usage helps family businesses identify and obtain more innovation opportunities by 

accessing external information and knowledge. For new professional farmers, the influence of social media 

embedding on innovation ability must undergo an intricate process, specifically involving the management of farmer 

knowledge. It is only through acquiring sufficient knowledge, learning to share, integrate, and apply that knowledge, 

that the innovation ability of farmers can genuinely improve. Based on the above research, the following hypothesis 

is drawn: 

H6: Knowledge management plays a significant mediating role between social media embedding and the innovation 

ability of new professional farmers. 

H6a: Knowledge management plays a significant mediating role between network media embedding and the 

innovation ability of new professional farmers. 

H6b: Knowledge management plays a significant mediating role between network community embedding and the 

innovation ability of new professional farmers. 



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2.8. The Regulatory Effect of Learning Orientation 

Learning orientation (LO) is a crucial strategic orientation that empowers companies to better acquire external 

knowledge, transform and utilize it, induce changes in their thinking patterns and behavior, and enhance the likelihood 

of engaging in innovative and proactive activities [39]. The significant impact of learning orientation on knowledge 

management has been generally verified. According to Alerasoul et al. [71], in specific social network relationships, 

companies with a strong learning orientation have a clearer purpose in knowledge searches. They are more proactive in 

obtaining information and are more likely to discover valuable knowledge. They also use their subjective initiative to 

seize opportunities, match, integrate, and utilize knowledge, and even create knowledge again. Research on small and 

medium-sized enterprises (SMEs) found that learning orientation affects the acquisition and utilization of operational 

information and knowledge, thereby influencing the performance of SMEs [72]. Cross-border search is employed to 

gather various resources, including knowledge, related to the study of innovative behavior in new ventures. Learning 

orientation plays a moderating role, and the more LO-oriented enterprises are, the stronger their cross-border integration 

ability is, ultimately leading to better innovation performance [73]. Alnuaimi et al. [74] proposed that in the IT 

environment, companies with a stronger LO are more willing for employees to communicate, share knowledge, and 

integrate knowledge, which is beneficial for promoting enterprise performance. For new professional farmers, if their 

LO is stronger, they are more likely to use social media as a means to learn and carry out knowledge management. 

Ultimately, whether it is from online media or online communities, they can better acquire, integrate, and utilize relevant 

knowledge to serve agricultural entrepreneurship. Based on the above research and analysis, the following hypotheses 

are proposed: 

H7: LO plays a positive regulatory role between online media embedding and new professional farmer knowledge 

management. 

H8: LO plays a positive regulatory role between online community embedding and new professional farmer 

knowledge management. 

Based on the above, this study has developed the following conceptual model diagram (Figure 1). 

H1H3

H4

Knowledge 
Management

H5

H2
Network 

Community 
Embedding

Network Media 
Embedding

Innovative 
Ability of New 
Professional 

Farmers

Learning 
Orientation

 

Figure 1. Conceptual model diagram of this study (Note: The dotted lines represent the moderating effects) 

3. Research Design and Data Collection 

3.1. Variable Measurement and Questionnaire Design 

This study includes five variables: online media embedding, online community embedding, knowledge management, 

innovation ability, and learning orientation. The scales for online media embedding and online community embedding 

are adapted from Xie et al. [27], the scale for knowledge management is adapted from Mardani et al. [30], the scale for 

innovation ability is adapted from Borah et al. [75], and the scale for learning orientation is adapted from Wahyono & 

Hutahayan [49].  



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All measurement items are devised using a 7-point Likert scale, encompassing fully consistent, consistent, relatively 

consistent, general, relatively inconsistent, inconsistent, and fully inconsistent. The research team conducted interviews 

in the early stage to comprehend the relevant situations of the research variables. The questionnaire was crafted in 

conjunction with the specific rural context to refine the expression of the items and consulted experts in agricultural 

business operations. This detailed information bolsters the transparency and credibility of the research methodology, 

providing a clear rationale for the selection of parameters and the design of the measurement instruments. Moreover, it 

underscores the researchers' efforts to ensure the validity and reliability of the study findings within the agricultural 

context. The manuscript employed Harman's single-factor test to evaluate convergent bias. Prior to this analysis, 

reliability and validity tests were executed on the scales included in the survey questionnaire. Using SPSS 24.0, reliability 

analysis was carried out, revealing Cronbach's 𝛼 values for the five variables were higher than the others. These findings 

further fortify the methodological rigor of the study by demonstrating the reliability of the measurement instruments 

used to assess the variables of interest. Moreover, they provide assurance regarding the internal consistency of the 

questionnaire items, enhancing confidence in the validity of the research findings. The specific research variables and 

their measurement items in the questionnaire are presented in Appendix I. 

3.2. Data Collection 

The research team collected data through both offline and online questionnaires. The offline questionnaire was mainly 

distributed to fruit, animal husbandry, and vegetable classes of the new professional farmer training program in Fujian 

Province. These students are typical representatives of new professional farmers in various regions of Fujian Province 

and have received training in social media operations. Selecting them as research subjects enables a reflection of the 

current situation of social media use and innovative management among new professional farmers in China. The online 

questionnaire, on the other hand, was disseminated to teachers knowledgeable about individuals conducting rural e-

commerce training throughout the province, aiming to collect information on the responses of local training class 

students to the questionnaire. 

The research team distributed a total of 417 questionnaires, and 336 valid questionnaires were collected after 

excluding invalid responses, resulting in an effective questionnaire recovery rate of 80.58%. Among the respondents, 

177 were male, constituting 52.68%, and 159 were female, accounting for 47.32%. In terms of age, 191 people were 

aged 31 to 40, accounting for the highest proportion, reaching 56.85%. In terms of education, agricultural entrepreneurs 

with a high school (or technical secondary school) education had the highest proportion, with 187 people, accounting 

for 55.65%. Farmers with a junior high school education ranked second with 93 people, accounting for 27.68%, and 

those with a college education or above had 56 people, accounting for 16.67%. In terms of entrepreneurial activities, 238 

individuals engaged in large-scale planting and breeding, agricultural product processing, agricultural product sales, and 

agricultural tourism services were 238, 19, 56, and 23 people respectively. 

4. Data Analysis and Results  

4.1. Analysis of Convergent Bias  

In order to evaluate the potential impact of convergent bias, this study utilized Harman's single-factor test, a widely 

recognized method for detecting common method bias in survey research. Exploratory factor analysis was conducted on 

a dataset comprising 336 questionnaires, aiming to assess the extent to which variance in responses could be attributed 

to a single underlying factor. The analysis revealed that, in the absence of rotation, the first factor accounted for 46.731% 

of the total variance. Typically, if a single dominant factor explains more than 50% of the variance, it suggests the 

potential presence of common method bias. However, in this instance, the first factor explained less than 50% of the 

variance, indicating that common method bias was not a significant concern in this study. This finding enhances the 

credibility and validity of the research results, suggesting that the observed relationships among variables are unlikely 

to be distorted by methodological biases. 

4.2. Reliability and Validity Test   

The initial phase of this study entailed conducting thorough assessments of reliability and validity on the scales 

employed within the survey questionnaire. Utilizing SPSS 24.0, reliability analysis was performed to compute 

Cronbach's α values for each of the five variables: network media embedding, network community embedding, 

knowledge management, innovation ability, and learning orientation. The resulting Cronbach's α values were 0.878, 

0.772, 0.905, 0.890, and 0.879, respectively. These values surpassed the conventional threshold of 0.7, indicating strong 

internal consistency and reliability of the questionnaire scales. A Cronbach's α value exceeding 0.7 signifies that the 

items within each variable consistently measure the same underlying construct. Hence, the questionnaire demonstrates 

reliability in assessing the targeted constructs. A detailed summary of these reliability analysis outcomes is provided in 

Table 1, supporting the credibility of the measurement instrument employed in the study. 



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Table 1. Reliability and validity test results of each variable 

Variables Indicators Measurement items 
Factor loading 

coefficient 
CR AVE 

Cronbach’s 

α 

Network media 

embedding 

ME1 I use social media to disseminate information. 0.923 

0.894 0.739 0.878 ME2 I utilize social media to acquire information. 0.850 

ME3 
My entrepreneurial activities are dependent on the media 

functionality of social media. 
0.802 

Network 

community 

embedding 

CE1 I maintain close contact with friends on social media networks. 0.739 

0.776 0.537 0.772 CE2 I have many friends I can interact with on social media. 0.704 

CE3 
I can obtain a large amount of heterogeneous information from 

social media friends. 
0.754 

Knowledge 
management 

KM1 
I can acquire information on agricultural innovation and 

management. 
0.839 

0.907 0.661 0.905 

KM2 
I possess information required for agricultural innovation and 

management. 
0.871 

KM3 I share information on agricultural innovation and management. 0.788 

KM4 I integrate various types of agricultural innovation information. 0.816 

KM5 I utilize agricultural innovation information based on my needs. 0.744 

Innovative 

ability 

AI1 
I can flexibly respond to and solve agricultural management 

issues. 
0.871 

0.890 0.730 0.890 AI2 I can propose innovative agricultural management strategies. 0.863 

AI3 
My agricultural innovation and management have achieved 

relatively good results. 
0.829 

Learning 

orientation 

LO1 
I believe learning is important in agricultural innovation and 

management. 
0.810 

0.879 0.645 0.879 

LO2 
I regularly spend time learning how to improve agricultural 

innovation and management. 
0.755 

LO3 
I am willing to spend money to learn agricultural innovation and 

management. 
0.835 

LO4 
Learning is an essential component of my agricultural 

entrepreneurship. 
0.811 

After the examination of reliability and validity, this study employed AMOS 24.0 to execute confirmatory factor 

analysis on the examined variables. The outcomes, detailed in Table 2, unveiled average variance extracted (AVE) values 

for each variable: network media embedding, network community embedding, knowledge management, innovation 

ability, and learning orientation. These AVE values were determined to be 0.739, 0.537, 0.661, 0.730, and 0.645, 

correspondingly. It is noteworthy that all AVE values exceeded the prescribed threshold of 0.5 as advocated by scholars, 

indicative of satisfactory convergence validity for every variable. Subsequently, adopting the AVE method, the square 

root values of the AVE for each variable were juxtaposed with the Pearson correlation coefficients between variables to 

evaluate discriminant validity. The square root values of the AVE for each variable were 0.860, 0.733, 0.813, 0.854, and 

0.803, respectively, with correlation coefficients ranging from 0.217 to 0.686. Importantly, all correlation coefficients 

were found to be lesser than the square root values of the diagonal AVE, a finding that aligns with recommendations 

from scholars. This observation underscores the favorable discriminant validity of the study variables. Hence, the results 

of the confirmatory factor analysis validate the reliability and validity of the measurement model, showcasing 

satisfactory convergence and discriminant validity for all variables examined. Consequently, the dataset is deemed apt 

for further analysis, poised to provide robust insights into the relationships among the variables under scrutiny. 

Table 2. Variable correlation coefficients and discriminant validity 

 AVE 
Network Media 

Embedding 

Network Community 

Embedding 

Knowledge 

Management 

Innovative 

Ability 

Learning 

Orientation 

Network Media Embedding 0.739 0.860     

Network Community Embedding 0.537 0.677** 0.733    

Knowledge Management 0.661 0.663** 0.633** 0.813   

Innovative Ability 0.730 0.619** 0.553** 0.686** 0.854  

Learning Orientation 0.645 0.326** 0.395** 0.303** 0.217** 0.803 

Note: * p<0.05， ** p<0.01. The items on the boldface diagonal represent the square roots of the AVE; off-diagonal elements are the correlation estimates. AVE refers 

to the average variance extracted. 



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4.3. Structural Model Testing    

This study continued to use AMOS24.0 to test the fitness of the model, and the results are shown in Table 3. The chi-

square/degree of freedom (𝜒2/DF) value is 1.663, which is within the acceptable range of less than 3. The values of TLI, 

CFI, GFI, RFI, NFI, and IFI are 0.981, 0.985, 0.953, 0.954, 0.964, and 0.985, all greater than 0.9. The RMSEA value is 

0.044, which is less than 0.08. All fitness index values are within the acceptable range and meet the suggestions of 

scholars, indicating that the fitness of the model in this study is good and can proceed to the next step of testing. 

Table 3. Model fit 

Fitting index χ2/DF TLI CFI GFI RFI NFI IFI RMSEA 

Allowable range 1<χ2/DF<3 >0.9 >0.9 >0.9 >0.9 >0.9 >0.9 <0.08 

Study model fit 1.663 0.981 0.985 0.953 0.954 0.964 0.985 0.044 

4.4. Path Analysis Test 

The path relationships between variables in the research model were tested using AMOS24.0, and the results are 

detailed in Table 4. Firstly, social media embedding significantly and positively affects innovation ability (𝛽=0.277, 

p=0.029), supporting H1. This implies that the network media function of social media directly and positively impacts 

the innovation ability of new professional farmers. However, the path of network community embedding on the 

innovation ability of new professional farmers is not significant (𝛽=0.113, p=0.376), contradicting H2. This suggests 

that the network community function of social media does not directly and positively impact the innovation ability of 

new professional farmers. This may be because the farmers' abilities are relatively weak, and although they have formed 

communities, they have not fully utilized the communities, so the impact of communities on innovation cannot be 

realized. Secondly, in the path from social media embedding to knowledge management, network media embedding 

significantly positively affects knowledge management (𝛽=0.435, p=0.002), supporting H3, indicating that the network 

media function of social media can promote knowledge management. Network community embedding also significantly 

positively affects knowledge management (𝛽=0.563, p< 0.000), supporting H4. This indicates that the network 

community function of social media can also promote knowledge management. Finally, knowledge management 

significantly positively affects innovation ability (𝛽=0.511, p< 0.000), supporting H5. This suggests that enhancing 

knowledge management in the context of social media is beneficial for improving the innovation ability of new 

professional farmers. 

Table 4. Path analysis results 

Action path 
Unstandardized 

path coefficient 
SE z (CR) p 

Standardized 

path coefficient 
Hypothesis Result 

Network media embedding ⇒ Innovative ability 0.277 0.127 2.181 0.029 0.213 H1 Support 

Network community embedding ⇒ Innovative ability 0.113  0.127  0.886  0.376  0.102  H2 Not support 

Network media embedding ⇒ Knowledge management 0.435  0.140  3.099  0.002  0.322  H3 Support 

Network community embedding ⇒ Knowledge management 0.563  0.130  4.330  *** 0.490  H4 Support 

Knowledge management ⇒ Innovative ability 0.511  0.078  6.566  *** 0.529  H5 Support 

Note: *** p<0.001. 

4.5. Mediating Effect Test 

This study continued to utilize the bootstrap method (Bootstrapping) to analyze the mediating effect mechanism of 

social media embedding on the innovation ability of new professional farmers in AMOS 24.0 software. A 95% 

confidence level was chosen, and 5000 repeated samplings were set to obtain the results shown in Table 5. In the 

"network media embedding⇒knowledge management⇒innovation ability" path, the point estimate of the mediating 

effect of knowledge management was 0.223, and the Z value was 2.398. The 95% Bootstrap interval was [0.066, 0.434], 

indicating that the mediating effect of knowledge management reached a significant level, supporting H6a. In the 

"network community embedding⇒knowledge management⇒innovation ability" path, the mediating effect of knowledge 

management was 0.289, and the Z value was 2.558. The 95% Bootstrap interval was [0.124, 0.573], indicating that the 

mediating effect of knowledge management also reached a significant level, supporting H6b. The total mediating effect 

of the two paths was 0.512, and the Z value was 4.833. The 95% Bootstrap interval was [0.300, 0.722], indicating that 

the total mediating effect of knowledge management reached a significant level, supporting H6. Therefore, knowledge 

management played a significant mediating role between social media embedding and the innovation ability of new 

professional farmers. Based on Table 4 and Table 5, in the "network media embedding⇒innovation ability" path, there 

were both direct and mediating paths, so knowledge management played a partial mediating role. In the "network 



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community embedding⇒innovation ability" path, the direct path effect was not significant, but the mediating effect was 

significant, indicating that network community embedding completely influenced innovation ability through the 

mediating effect of knowledge management. In Table 5, the total effect of social media embedding on innovation ability 

was 0.904, and the Z value was 12.556. The 95% Bootstrap interval was [0.762, 1.046], indicating that the total effect 

of social media embedding on innovation ability reached a significant level. 

Table 5. Analysis of Direct Effects, Mediated Effects, and Total Effects 

Effects Item Effect Coefficient SE z p LLCI ULCI 

Mediated 
Effects 

Network media embedding⇒Knowledge 

management⇒Innovative ability 
0.223  0.093  2.398  0.013  0.066  0.434  

Network community embedding⇒Knowledge 

management⇒Innovative ability 
0.289  0.113  2.558  0.000  0.124  0.573  

Total Mediated Effects 0.512  0.106  4.830  0.000  0.300  0.722  

Direct Effects 0.392  0.120  3.267  0.001  0.167  0.647  

Total Effects 0.904  0.072  12.556  0.000  0.762  1.046  

Note: Boot LLCI refers to the lower limit of the 95% confidence interval obtained by Bootstrap sampling, and Boot ULCI refers to the upper limit of the 95% 

confidence interval obtained by Bootstrap sampling. 

4.6. Moderating Effect Test 

This study incorporated learning orientation as a moderating variable to analyze its impact on the relationship between 

network media embedding and knowledge management, as well as the relationship between network community 

embedding and knowledge management. The results presented in Table 6 reveal that when the dependent variable is 

knowledge management, the moderating effect of "network media embedding * learning orientation" is 0.223 (t = 6.782, 

p < 0.000), indicating the presence of a moderating effect. For every increase of 1 unit in the moderator variable learning 

orientation, the influence of network media embedding on knowledge management will increase by 0.223 units, 

supporting research hypothesis H7. Additionally, the moderating effect of "network community embedding * learning 

orientation" is 0.075 (t = 0.035, p = 0.034), indicating the existence of a moderating effect. For every increase of 1 unit 

in the moderator variable learning orientation, the influence of network community embedding on knowledge 

management will increase by 0.075 units, supporting research hypothesis H8. In summary, both network media 

embedding and network community embedding are affected by learning orientation in their impacts on knowledge 

management. According to the data analysis results, learning orientation has a more pronounced moderating effect on 

the relationship between media embedding and knowledge management. The more farmers adopt a learning attitude 

toward social media, the better they can engage in knowledge management in both media and communities. 

Table 6. Analysis of Moderating Effects 

Dependent variable Independent variable β Standard error t p 

knowledge management 

Network media embedding 0.826 0.049 16.989 *** 

Learning orientation 0.174 0.040 4.353 *** 

Network media embedding*Learning orientation 0.223 0.033 6.782 *** 

network community embedding 0.683 0.052 13.180 *** 

Learning orientation 0.076 0.044 1.728 0.085 

Network community embedding*Learning orientation 0.075 0.035 2.133 0.034 

Note: *** p<0.001. 

5. Conclusions 

Based on embedding theory and knowledge management theory, this study empirically examined the relationship 

between social media embedding, knowledge management, learning orientation, and the innovative ability of new 

professional farmers. 

The following conclusions were drawn: Firstly, social media embedding significantly positively influences the 

innovative ability of new professional farmers. This finding aligns with a similar study conducted by Muninger et al. 

[76]. According to Muninger et al. [76], businesses increasingly employ social media for innovation, yet existing 

literature lacks comprehensive guidance for devising strategic approaches. Muninger et al. [76] adopted a qualitative, 

theory-building methodology to develop a conceptual framework of capabilities conducive to leveraging social media 

throughout the innovation lifecycle. The framework identified three critical capabilities and associated resources, 



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including social media management for coordinating activities across innovation stages, executive leadership for 

fostering support and enabling agile decision-making, and flexible processes facilitating rapid knowledge dissemination 

and experimentation. Muninger et al.'s [76] contribution enhanced organizational capability theory in the innovation 

context by offering practical insights for managerial implementation of social media strategies. 

New professional farmers can directly enhance their innovative ability through the media function of social media. 

However, the tangible impact of network community embedding on their innovative ability is less apparent. This 

outcome aligns with a study conducted by Hafkesbrink & Schroll [21]. Hafkesbrink & Schroll [21] outlined a novel 

approach to innovation in the Digital Economy, termed "Embedded Innovation" or Innovation 3.0. It introduced the 

concept of "embeddedness" to highlight the growing importance of integrating firms into their surrounding communities 

to effectively absorb valuable knowledge. Hafkesbrink & Schroll [21] discussed the evolutionary transition from Closed 

to Open to Embedded Innovation in small and medium-sized enterprises (SMEs), shedding light on the varying modes 

of learning from communities based on firm relationships and knowledge flows.  

Third, knowledge management plays a significant mediating role between social media embedding and the innovative 

ability of new professional farmers. This outcome resonates with a previous study conducted by Sapta et al. [77], where 

knowledge management also served as a mediating variable. Sapta et al. [77] investigated how organizational culture 

and leadership styles influenced knowledge management and sustainable performance. Additionally, Sapta et al.’s [77] 

study examined knowledge management's role as a mediator between organizational culture, leadership style, and 

sustainable performance, revealing the significant effects of organizational culture and transformational leadership on 

knowledge management. Furthermore, knowledge management was found to mediate the relationships between 

organizational culture, leadership style, and sustainable performance. These findings supported knowledge-based theory 

regarding knowledge management practices and sustainable performance, emphasizing the importance of organizational 

culture and transformational leadership in traditional organizations. Based on the above discussion and prior research 

comparison regarding knowledge management, it can be inferred that after using social media, new professional farmers 

can better manage knowledge through both network media embedding and network community embedding and thereby 

improve their innovative ability. Notably, in the network community embedding mode, the mere existence of the 

community does not inherently foster innovative development for farmers. It is through active participation, involving 

obtaining, sharing, integrating, and utilizing knowledge within the community, that they can effectively enhance their 

innovative ability. 

Fourth, learning orientation positively moderates the relationship between network media embedding, network 

community embedding, and knowledge management. The results can be compared to prior research by Türk et al. [78] 

that similarly employed learning orientation in a moderating role. Türk et al. [78], drawing on social learning theory, 

explored the impact of prior entrepreneurial exposure on entrepreneurial passion and examined how an individual's 

learning orientation moderated this relationship. The study collected data from students across various disciplines to 

empirically validate the research model. Türk et al.’s [78] findings indicated that both types of prior entrepreneurial 

exposure positively influence entrepreneurial passion, with medium to high levels of learning orientation reinforcing 

these relationships. Building on this discussion, it can be posited that farmers with a stronger learning orientation are 

better equipped to actively leverage the media function of social media, influencing knowledge management, and 

effectively obtaining and utilizing knowledge through community relationships. This, in turn, leads to an enhancement 

of their innovation ability. 

5.1. Managerial Implications   

Agricultural sustainable development relies on high-quality farmers and continuous improvement of their innovative 

ability. The relevant conclusions of this study can provide important insights for relevant governments and the cultivation 

of innovative ability of new professional farmers. Firstly, the government can create better conditions for the promotion 

of information technology in rural areas. To fully realize the role of social media tools in enhancing the innovative ability 

of farmers, it is necessary for governments at all levels to further improve the construction of rural grassroots network 

facilities, increase the promotion of smartphones, strengthen training for farmers, and guide more new professional 

farmers to use social media tools effectively. Secondly, new professional farmers should enhance their media literacy, 

recognizing the influence of social media through network media embedding and network community embedding. 

Entrepreneurial farmers ought to strengthen their understanding of social media tools, continually refine their learning 

of social media tools, continuously improve their media operation skills, and be able to flexibly use the different functions 

of social media according to the needs of agricultural innovation. Thirdly, new professional farmers should cultivate 

awareness of using social media to carry out knowledge management. Knowledge management is a prerequisite for 

innovation. Given the positive role of social media in knowledge management, new professional farmers can learn to 

use social media tools to strengthen the acquisition, integration, and utilization of agricultural innovation knowledge 

through self-study, attending training classes, or learning from peers, and improving their agricultural innovation 

knowledge reserves. Finally, new professional farmers should adopt a learning-oriented management philosophy when 



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using social media. Recognizing the dual nature of social media, farmers need to avoid ineffective embedding that may 

lead to wasted time and energy. Instead, they should use social media as an entrepreneurial tool, consistently engage in 

lifelong learning, adapt to environmental changes, enhance their abilities, and fully leverage the positive functions of 

social media for promoting agricultural knowledge management and innovation. This approach will contribute to the 

sustainable development of agricultural entrepreneurship. 

5.2. Limitations and Future Research   

This study offers both theoretical and empirical evidence for new professional farmers to enhance their innovative 

abilities through social media tools, providing significant implications for fostering rural innovation and 

entrepreneurship activities. However, several aspects of the impact of information technology on the innovative behavior 

of new professional farmers warrant further analysis. This includes exploring the potential existence of other mediating 

variables between social media embedding and farmers' innovative abilities, examining how factors related to 

information technology tools may influence the effectiveness of farmers' social media usage, and conducting more in-

depth research on the role of different modes of social media embedding in specific agricultural innovation behaviors 

(technical innovation, management innovation, etc.). Information technology represented by social media will 

profoundly affect the innovative development of agriculture and rural areas. In the future, academia can further explore 

more issues in related fields. 

6. Declarations  

6.1. Author Contributions 

Conceptualization, G.X. and X.S.; methodology, G.X. and X.S.; software, G.X.; validation, X.S. and M.H.; formal 

analysis, G.X. and X.S.; investigation, G.X.; writing—original draft preparation, G.X., X.S., and M.H.; writing—review 

and editing, G.X., X.S., and M.H.; visualization, X.S. and M.H.; supervision, M.H. All authors have read and agreed to 

the published version of the manuscript. 

6.2. Data Availability Statement 

The data presented in this study are available in the article. 

6.3. Funding 

This research was funded by Natural Science Foundation Project of Fujian Science and Technology Department 

(Grant Number: 2021J011247) and Fujian Business College Digital Smart Retail Management Scientific Research 

Innovation Team Support Plan (Grant Number: CXTD202303). 

6.4. Institutional Review Board Statement 

Not applicable.   

6.5. Informed Consent Statement 

Not applicable.  

6.6. Declaration of Competing Interest 

The authors declare that they have no known competing financial interests or personal relationships that could have 

appeared to influence the work reported in this paper. 

7. References 

[1] Anichkina, O., Tatochenko, A., Tatochenko, I., & Chernegov, N. (2019). Development strategy of agricultural enterprises in the 

production of high-tech products. IOP Conference Series: Earth and Environmental Science, 403(1), 012133. doi:10.1088/1755-

1315/403/1/012133. 

[2] Sáenz, J., Alcalde-Heras, H., Aramburu, N., & Buenechea-Elberdin, M. (2024). Boosting innovativeness in organic farming: the 

role of external relational capital. Journal of Intellectual Capital, 25(1), 143–165. doi:10.1108/JIC-11-2022-0229. 

[3] Sharma, V., Tripathi, A. K., & Mittal, H. (2022). Technological revolutions in smart farming: Current trends, challenges & future 

directions. Computers and Electronics in Agriculture, 201, 107217. doi:10.1016/j.compag.2022.107217. 

[4] Semin, A., Mironov, D., Kislitskiy, M., Zasypkin, A., & Ivanov, V. (2023). Improving the Theoretical and Methodological 

Framework for Implementing Digital Twin Technology in Various Sectors of Agriculture. Emerging Science Journal, 7(4), 1100-

1115. doi:10.28991/ESJ-2023-07-04-05. 



HighTech and Innovation Journal         Vol. 5, No. 2, June, 2024 

307 

 

[5] Noerlina, Mursitama, T. N., Simatupang, B., & Bandur, A. (2022). The Importance of Interfirm Networks in Enhancing Innovation 

Capability and Exporting in High-Tech Industry. HighTech and Innovation Journal, 3(Special Issue), 52–64. doi:10.28991/HIJ-

SP2022-03-05. 

[6] Zhao, D., Chen, Y., Parolin, B., & Fan, X. (2019). New Professional Farmers’ Training (NPFT): A multivariate analysis of 

farmers’ participation in lifelong learning in Shaanxi, China. International Review of Education, 65(4), 579–604. 

doi:10.1007/s11159-019-09790-5. 

[7] Chen, L., & Chen, Z. (2020). Analysis on the Competency of New Professional Farmers and Its Influencing Factors. IOP 

Conference Series: Earth and Environmental Science, 598(1), 012040. IOP Publishing. doi:10.1088/1755-1315/598/1/012040. 

[8] Tomich, T. P., Lidder, P., Coley, M., Gollin, D., Meinzen-Dick, R., Webb, P., & Carberry, P. (2019). Food and agricultural 

innovation pathways for prosperity. Agricultural Systems, 172, 1–15. doi:10.1016/j.agsy.2018.01.002. 

[9] Blakeney, M. (2022). Agricultural Innovation and Sustainable Development. Sustainability (Switzerland), 14(5), 2698. 

doi:10.3390/su14052698. 

[10] Purwati, A. A., Budiyanto, Suhermin, & Hamzah, M. L. (2020). The effect of innovation capability on business performance: 

the role of social capital and entrepreneurial leadership on SMEs in Indonesia. Accounting, 7(2), 323–330. 

doi:10.5267/j.ac.2020.11.021. 

[11] Ferreira, J., & Coelho, A. (2020). Dynamic capabilities, innovation and branding capabilities and their impact on competitive 

advantage and SME’s performance in Portugal: the moderating effects of entrepreneurial orientation. International Journal of 

Innovation Science, 12(3), 255–286. doi:10.1108/IJIS-10-2018-0108. 

[12] Zhang, L., & Wu, B. (2018). Farmer innovation system and government intervention: An empirical study of straw utilisation 

technology development and diffusion in China. Journal of Cleaner Production, 188, 698–707. doi:10.1016/j.jclepro.2018.03.224. 

[13] Kreft, C., Huber, R., Wuepper, D., & Finger, R. (2021). The role of non-cognitive skills in farmers’ adoption of climate change 

mitigation measures. Ecological Economics, 189, 107169. doi:10.1016/j.ecolecon.2021.107169. 

[14] Niu, Z., Chen, C., Gao, Y., Wang, Y., Chen, Y., & Zhao, K. (2022). Peer effects, attention allocation and farmers’ adoption of 

cleaner production technology: Taking green control techniques as an example. Journal of Cleaner Production, 339, 130700. 

doi:10.1016/j.jclepro.2022.130700. 

[15] Awuor, F. M., & Rambim, D. A. (2022). Adoption of ICT-in-Agriculture Innovations by Smallholder Farmers in Kenya. 

Technology and Investment, 13(03), 92-103. doi:10.4236/ti.2022.133007. 

[16] Raza, M. H., Khan, G. A., Shahbaz, B., & Saleem, M. F. (2020). Effectiveness of information and communication technologies 

as information source among farmers in Pakistan. Pakistan Journal of Agricultural Sciences, 57(1), 281–288. 

doi:10.21162/PAKJAS/19.8292. 

[17] Li, Z., Huang, Y., Yang, Y., & Lai, X. (2023). Can TikTok be a good way to extend the adoption of green control techniques? 

Evidence from rural China. Asian Journal of Technology Innovation, 31(3), 711–728. doi:10.1080/19761597.2022.2148704. 

[18] Wang, S., Tang, J., Aggarwal, C., Chang, Y., & Liu, H. (2017). Signed network embedding in social media. Proceedings of the 

17th SIAM International Conference on Data Mining, SDM 2017, 327–335. doi:10.1137/1.9781611974973.37. 

[19] Ge, C., Lv, W., & Wang, J. (2023). The Impact of Digital Technology Innovation Network Embedding on Firms’ Innovation 

Performance: The Role of Knowledge Acquisition and Digital Transformation. Sustainability (Switzerland), 15(8), 6938. 

doi:10.3390/su15086938. 

[20] Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., & Yang, S. (2017). Community preserving network embedding. 31st AAAI 

Conference on Artificial Intelligence, AAAI 2017, 203–209. doi:10.1609/aaai.v31i1.10488. 

[21] Hafkesbrink, J., & Schroll, M. (2011). Innovation 3.0: embedding into community knowledge - collaborative organizational 

learning beyond open innovation. Journal of Innovation Economics & Management, 7(1), 55–92. doi:10.3917/jie.007.0055. 

[22] Girard, N. (2015). Knowledge at the boundary between science and society: a review of the use of farmers’ knowledge in 

agricultural development. Journal of Knowledge Management, 19(5), 949–967. doi:10.1108/JKM-02-2015-0049. 

[23] Ureña-Espaillat, H. J., Briones-Peñalver, A. J., Bernal-Conesa, J. A., & Córdoba-Pachón, J. R. (2023). Knowledge and innovation 

management in agribusiness: A study in the Dominican Republic. Business Strategy and the Environment, 32(4), 2008–2021. 

doi:10.1002/bse.3233. 

[24] Tseng, S. M. (2008). The effects of information technology on knowledge management systems. Expert Systems with 

Applications, 35(1–2), 150–160. doi:10.1016/j.eswa.2007.06.011. 

[25] Singh Nain, M., Singh, R., & Mishra, J. R. (2019). Social networking of innovative farmers through WhatsApp messenger for 

learning exchange: A study of content sharing. Indian Journal of Agricultural Sciences, 89(3), 556–558. 

doi:10.56093/ijas.v89i3.87605. 



HighTech and Innovation Journal         Vol. 5, No. 2, June, 2024 

308 

 

[26] Contreras-medina, D. I., Medina-cuéllar, S. E., Sánchez-gómez, J., & Rodríguez-peralta, C. M. (2021). Innovation of women 

farmers: A technological proposal for mezcalilleras sustainability in Mexico, based on knowledge management. Sustainability 

(Switzerland), 13(21), 11706. doi:10.3390/su132111706. 

[27] Xie, G. H., Wang, L. P., & Khan, A. (2021). An Assessment of Social Media Usage Patterns and Social Capital: Empirical 

Evidence from the Agricultural Systems of China. Frontiers in Psychology, 12, 4973. doi:10.3389/fpsyg.2021.767357. 

[28] Liu, T., & Tang, L. (2020). Open innovation from the perspective of network embedding: knowledge evolution and development 

trend. Scientometrics, 124(2), 1053–1080. doi:10.1007/s11192-020-03520-7. 

[29] An, X., Deng, H., Chao, L., & Bai, W. (2014). Knowledge management in supporting collaborative innovation community 

capacity building. Journal of Knowledge Management, 18(3), 574–590. doi:10.1108/JKM-10-2013-0413. 

[30] Mardani, A., Nikoosokhan, S., Moradi, M., & Doustar, M. (2018). The Relationship Between Knowledge Management and 

Innovation Performance. Journal of High Technology Management Research, 29(1), 12–26. doi:10.1016/j.hitech.2018.04.002. 

[31] Dasgupta, M., Sahay, A., & Gupta, R. K. (2009). The role of knowledge management in innovation. Journal of Information and 

Knowledge Management, 8(4), 317–330. doi:10.1142/S0219649209002427. 

[32] Daniele, B. C., Barbara, S., Isabel, B., & Alberto, G. (2022). Analysis of perceived robustness, adaptability and transformability 

of Spanish extensive livestock farms under alternative challenging scenarios. Agricultural Systems, 202, 103487. 

doi:10.1016/j.agsy.2022.103487. 

[33] Ro, Y. J., Yoo, M., Koo, Y., & Song, J. H. (2021). The roles of learning orientation: structural determinants for improving 

knowledge sharing with committed and satisfied employees. Industrial and Commercial Training, 53(1), 60–76. 

doi:10.1108/ICT-10-2019-0094. 

[34] Xie, G. H., Wang, L. P., & Lee, B. F. (2021). Understanding the Impact of Social Capital on Entrepreneurship Performance: The 

Moderation Effects of Opportunity Recognition and Operational Competency. Frontiers in Psychology, 12, 687205. 

doi:10.3389/fpsyg.2021.687205. 

[35] Granovetter, M. (2018). Economic action and social structure: The problem of embeddedness. The Sociology of Economic Life, 

Third Edition, 91(3), 22–45. doi:10.4324/9780429494338. 

[36] Yongyun, Z., Shengtai, Z., & Cuihua, W. Embed or involved, Why Crowdsourcing individual contribute knowledge? Science 

Research Management, 38(5), 30. 

[37] Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business 

Horizons, 53(1), 59–68. doi:10.1016/j.bushor.2009.09.003. 

[38] Steel, G. (2017). Navigating (im)mobility: Female entrepreneurship and social media in Khartoum. Africa, 87(2), 233–252. 

doi:10.1017/S0001972016000930. 

[39] Shekhar, & Valeri, M. (2023). Trends in knowledge management research in small businesses. European Business Review, 

35(5), 573–599. doi:10.1108/EBR-10-2022-0200. 

[40] Kommey, R., & Fombad, M. (2023). Strategies for Knowledge Sharing Among Rice Farmers: A Ghanaian Perspective. 

Electronic Journal of Knowledge Management, 21(2), 114–129. doi:10.34190/EJKM.21.2.2803. 

[41] Below, T., Artner, A., Siebert, R., & Sieber, S. (2010). Micro-level practices to adapt to climate change for African small-scale 

farmers: a review of selected literature. IFPRI-Discussion Papers, (953), 1-20. 

[42] Griffin, C., Wreford, A., & Cradock-Henry, N. A. (2023). ‘As a farmer you’ve just got to learn to cope’: Understanding dairy 

farmers’ perceptions of climate change and adaptation decisions in the lower South Island of Aotearoa-New Zealand. Journal of 

Rural Studies, 98, 147–158. doi:10.1016/j.jrurstud.2023.02.001. 

[43] Meijer, S. S., Catacutan, D., Ajayi, O. C., Sileshi, G. W., & Nieuwenhuis, M. (2015). The role of knowledge, attitudes and 

perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub-Saharan Africa. 

International Journal of Agricultural Sustainability, 13(1), 40–54. doi:10.1080/14735903.2014.912493. 

[44] Ayoub, M. (2023). One size does not fit all: The plurality of knowledge sources for transition to sustainable farming. Journal of 

Rural Studies, 97, 243–254. doi:10.1016/j.jrurstud.2022.12.007. 

[45] Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. 

Industrial Marketing Management, 31(6), 515–524. doi:10.1016/S0019-8501(01)00203-6. 

[46] Meekaewkunchorn, N., Szczepańska-Woszczyna, K., Muangmee, C., Kassakorn, N., & Khalid, B. (2021). Entrepreneurial 

orientation and sme performance: The mediating role of learning orientation. Economics and Sociology, 14(2), 294–312. 

doi:10.14254/2071-789X.2021/14-2/16. 



HighTech and Innovation Journal         Vol. 5, No. 2, June, 2024 

309 

 

[47] Sheng, M. L., & Chien, I. (2016). Rethinking organizational learning orientation on radical and incremental innovation in high-

tech firms. Journal of Business Research, 69(6), 2302–2308. doi:10.1016/j.jbusres.2015.12.046. 

[48] Liao, S. H., Chen, C. C., Hu, D. C., Chung, Y. Chun, & Yang, M. J. (2017). Developing a sustainable competitive advantage: 

absorptive capacity, knowledge transfer and organizational learning. Journal of Technology Transfer, 42(6), 1431–1450. 

doi:10.1007/s10961-016-9532-1. 

[49] Wahyono, & Hutahayan, B. (2021). The relationships between market orientation, learning orientation, financial literacy, on the 

knowledge competence, innovation, and performance of small and medium textile industries in Java and Bali. Asia Pacific 

Management Review, 26(1), 39–46. doi:10.1016/j.apmrv.2020.07.001. 

[50] Straub, D., Weill, P., & Schwaig, K. S. (2009). Strategic dependence on the IT resource and outsourcing: A test of the strategic 

control model. In Information Systems Outsourcing (Third Edition): Enduring Themes, Global Challenges, and Process 

Opportunities. Springer, Berlin, Germany, 175-199. doi:10.1007/978-3-540-88851-2_8. 

[51] Secundo, G., Del Vecchio, P., & Mele, G. (2021). Social media for entrepreneurship: myth or reality? A structured literature 

review and a future research agenda. International Journal of Entrepreneurial Behaviour and Research, 27(1), 149–177. 

doi:10.1108/IJEBR-07-2020-0453. 

[52] Naik, H. S., & Möslein, K. M. (2016). Social media in user entrepreneurship. Lecture Notes in Computer Science (Including 

Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9742, 428–436. doi:10.1007/978-3-319-

39910-2_40. 

[53] Chowdhury, A., Odame, H. H., Thompson, S., & Hauser, M. (2015). Enhancing farmers’ capacity for botanical pesticide 

innovation through video-mediated learning in Bangladesh. International Journal of Agricultural Sustainability, 13(4), 326–349. 

doi:10.1080/14735903.2014.997461. 

[54] Bhimani, H., Mention, A. L., & Barlatier, P. J. (2019). Social media and innovation: A systematic literature review and future 

research directions. Technological Forecasting and Social Change, 144, 251–269. doi:10.1016/j.techfore.2018.10.007. 

[55] Torres de Oliveira, R., Indulska, M., Steen, J., & Verreynne, M. L. (2020). Towards a framework for innovation in retailing 

through social media. Journal of Retailing and Consumer Services, 54, 101772. doi:10.1016/j.jretconser.2019.01.017. 

[56] Cepeda-Carrion, I., Ortega-Gutierrez, J., Garrido-Moreno, A., & Cegarra-Navarro, J. G. (2023). The Mediating Role of 

Knowledge Creation Processes in the Relationship Between Social Media and Open Innovation. Journal of the Knowledge 

Economy, 14(2), 1275–1297. doi:10.1007/s13132-022-00949-4. 

[57] Martini, E., Roshetko, J. M., & Paramita, E. (2017). Can farmer-to-farmer communication boost the dissemination of agroforestry 

innovations? A case study from Sulawesi, Indonesia. Agroforestry Systems, 91(5), 811–824. doi:10.1007/s10457-016-0011-3. 

[58] Young Choi, S., Lee, H., & Yoo, Y. (2010). The impact of information technology and transactive memory systems on knowledge 

sharing, application, and team performance: A field study. MIS Quarterly: Management Information Systems, 34(4), 833–854. 

doi:10.2307/25750708. 

[59] Mills, J., Reed, M., Skaalsveen, K., & Ingram, J. (2019). The use of Twitter for knowledge exchange on sustainable soil 

management. Soil Use and Management, 35(1), 195–203. doi:10.1111/sum.12485. 

[60] Tambo, J. A., Mugambi, I., Onyango, D. O., Uzayisenga, B., & Romney, D. (2023). Using mass media campaigns to change 

pesticide use behaviour among smallholder farmers in East Africa. Journal of Rural Studies, 99, 79–91. 

doi:10.1016/j.jrurstud.2023.03.001. 

[61] Soares, N. D., Braga, R., David, J. M. N., Siqueira, K. B., & Stroele, V. (2023). Data Analysis in Social Networks for 

Agribusiness: A Systematic Review. IEEE Access, 11, 8422–8432. doi:10.1109/ACCESS.2023.3237984. 

[62] Giulivi, N., Harou, A. P., Gautam, S., & Guereña, D. (2023). Getting the message out: Information and communication 

technologies and agricultural extension. American Journal of Agricultural Economics, 105(3), 1011–1045. doi:10.1111/ajae.12348. 

[63] Olkiewicz, M. (2015). Knowledge management as a determinant of innovation in enterprises. Proceedings of the 9th International 

Management Conference Management and Innovation for Competitive Advantage, Bucharest, Romania, 399-409. 

[64] Zhao, J. (2021). Knowledge management capability and technology uncertainty: driving factors of dual innovation. Technology 

Analysis and Strategic Management, 33(7), 783–796. doi:10.1080/09537325.2020.1841896. 

[65] Li, S., Han, S., & Shen, T. (2019). How can a firm innovate when embedded in a cluster? Evidence from the automobile industrial 

cluster in China. Sustainability (Switzerland), 11(7), 1837. doi:10.3390/su11071837. 

[66] Blázquez Puerta, C. D., Bermúdez-González, G., & Soler García, I. P. (2022). Human Systematic Innovation Helix: Knowledge 

Management, Emotional Intelligence and Entrepreneurial Competency. Sustainability (Switzerland), 14(7), 4296. 

doi:10.3390/su14074296. 



HighTech and Innovation Journal         Vol. 5, No. 2, June, 2024 

310 

 

[67] Dolinska, A., & d’Aquino, P. (2016). Farmers as agents in innovation systems. Empowering farmers for innovation through 

communities of practice. Agricultural Systems, 142, 122–130. doi:10.1016/j.agsy.2015.11.009. 

[68] Rosário, J., Madureira, L., Marques, C., & Silva, R. (2022). Understanding Farmers’ Adoption of Sustainable Agriculture 

Innovations: A Systematic Literature Review. Agronomy, 12(11), 2879. doi:10.3390/agronomy12112879. 

[69] Dey, B. L., Sarma, M., Pandit, A., Sarpong, D., Kumari, S., & Punjaisri, K. (2019). Social media led co-creation of knowledge 

in developing societies: SME’s roles in the adoption, use and appropriation of smartphones in South Asia. Production Planning 

and Control, 30(10–12), 1019–1031. doi:10.1080/09537287.2019.1582106. 

[70] Cofré-Bravo, G., Klerkx, L., & Engler, A. (2019). Combinations of bonding, bridging, and linking social capital for farm 

innovation: How farmers configure different support networks. Journal of Rural Studies, 69, 53–64. 

doi:10.1016/j.jrurstud.2019.04.004. 

[71] Alerasoul, S. A., Afeltra, G., Hakala, H., Minelli, E., & Strozzi, F. (2022). Organisational learning, learning organization, and 

learning orientation: An integrative review and framework. Human Resource Management Review, 32(3), 100854. 

doi:10.1016/j.hrmr.2021.100854. 

[72] Arshad, M. Z., Run, T. mei, & Ahmad, M. J. (2020). The Importance of Learning Orientation and Entrepreneurial Orientation 

on SMEs Performance Sustainable Development View Project Strategic Management View project. International Journal of 

Disaster Recovery and Business Continuity, 11(1), 1220–1228.  

[73] Zahoor, N., Donbesuur, F., Christofi, M., & Miri, D. (2022). Technological innovation and employee psychological well-being: 

The moderating role of employee learning orientation and perceived organizational support. Technological Forecasting and 

Social Change, 179, 121610. doi:10.1016/j.techfore.2022.121610. 

[74] Alnuaimi, M., Alzoubi, H. M., Ajelat, D., & Alzoubi, A. A. (2021). Towards intelligent organisations: An empirical investigation 

of learning orientation’s role in technical innovation. International Journal of Innovation and Learning, 29(2), 207–221. 

doi:10.1504/IJIL.2021.112996. 

[75] Borah, P. S., Iqbal, S., & Akhtar, S. (2022). Linking social media usage and SME’s sustainable performance: The role of digital 

leadership and innovation capabilities. Technology in Society, 68, 101900. doi:10.1016/j.techsoc.2022.101900. 

[76] Muninger, M. I., Hammedi, W., & Mahr, D. (2019). The value of social media for innovation: A capability perspective. Journal 

of Business Research, 95, 116–127. doi:10.1016/j.jbusres.2018.10.012. 

[77] Sapta, I. K. S., Sudja, I. N., Landra, I. N., & Rustiarini, N. W. (2021). Sustainability performance of organization: Mediating role 

of knowledge management. Economies, 9(3), 97. doi:10.3390/economies9030097. 

[78] Türk, S., Zapkau, F. B., & Schwens, C. (2020). Prior entrepreneurial exposure and the emergence of entrepreneurial passion: 

The moderating role of learning orientation. Journal of Small Business Management, 58(2), 225–258. 

doi:10.1080/00472778.2019.1659678. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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Appendix I 

Questionnaire Items 

Variables Indicators Measurement items References 

Network media 
embedding 

ME1 I use social media to disseminate information. 

Xie et al. [27] ME2 I use social media to acquire information. 

ME3 My entrepreneurial activities are dependent on the media functionality of social media. 

Network community 

embedding 

CE1 I maintain close contact with friends on social media networks. 

Xie et al. [27] CE2 I have many friends I can interact with on social media. 

CE3 I can obtain a large amount of heterogeneous information from social media friends. 

Knowledge 

management 

KM1 I can acquire information on agricultural innovation and management. 

Mardani et al. 

[30] 

KM2 I possess information required for agricultural innovation and management. 

KM3 I share information on agricultural innovation and management. 

KM4 I integrate various types of agricultural innovation information. 

KM5 I utilize agricultural innovation information based on my needs. 

Innovative ability 

AI1 I am able to flexibly respond to and solve agricultural management issues. 

Borah et al. 

[75] 
AI2 I am able to propose innovative agricultural management strategies. 

AI3 My agricultural innovation and management have achieved relatively good results. 

Learning orientation 

ES1 I believe learning is important in agricultural innovation and management. 

Hutahayan 

[49] 

ES2 I regularly spend time learning how to improve agricultural innovation and management. 

ES3 I am willing to spend money to learn agricultural innovation and management. 

ES4 Learning is an essential component of my agricultural entrepreneurship.