Digital literacy and farmers’ entrepreneurial behavior—Empirical analysis based on CHFS2019 micro data

Bai, Qingyun; Chen, Haipeng; Zhou, Jie; Li, Guohong; Zang, Dundang; Sow, Yaya; Shen, Qianling

doi:10.1371/journal.pone.0288245

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…The utilization of mobile devices for Internet access was assessed in the CFPS2020 questionnaire to gauge farmers' engagement with digital technology, which was expressed as "Inuse". At the same time, considering the differences in the ability of farmers to use digital technology [40,45,61], "Instudy" and "Information" served as the secondary indicators of the basis of digital technology use and are defined as the extent of utilization of digital technology. This provides a comprehensive assessment of digital literacy level based on the extent of digital technology utilization.…”

Section: Primary Explanatory Variablesmentioning

confidence: 99%

Digital Literacy Level and Formal Credit Constraints: Probit Analysis of Farm Households’ Borrowing Behavior in China

Zhou,

Li,

Chen

et al. 2024

Agriculture

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With the rapid evolution of the rural digital economy, analyzing the impact of digital literacy level on farm households’ formal borrowing is crucial for easing credit constraints and fostering rural economic growth. Leveraging the data from the 2020 China Family Panel Studies (CFPSs) and applying binary probit models and the Karlson–Holm–Breen (KHB) method, this study delineates the positive correlation between the digital literacy level and increased formal borrowing among farm households. The findings, which were robust against endogeneity and robustness tests, underscore the role of digital literacy level in augmenting farmers’ earnings and social networks, with a notably stronger mediation by earnings. The effects are particularly significant for middle-aged and older, less educated males in the central and western regions, in contrast with younger, highly educated females in the east. This research advocates for enhancing rural digital infrastructure and education, alongside financial system reforms, to advance rural financial development and support sustainable rural revitalization.

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Section: Primary Explanatory Variablesmentioning

confidence: 99%

Digital Literacy Level and Formal Credit Constraints: Probit Analysis of Farm Households’ Borrowing Behavior in China

Zhou,

Li,

Chen

et al. 2024

Agriculture

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“…Farmers with better socio-demographic characteristics tend to have good digital literacy. Besides, farmers with higher digital literacy can use various digital tools to understand and master the latest technology, providing technical support for their entrepreneurship (Bai et al, 2023).The use of this application is suitable for farmers who have the motivation to expand market access. Therefore, farmers with less education can still use this application because the application is designed for non-expert users so that the menu display is kept as minimal as possible for easy operation.…”

Section: Mobile App Technology Acceptance Based On Farmers Backgroundmentioning

confidence: 99%

Technological Acceptance of Cattle Farmers in Mobile Applications for Livestock Digital Marketing

Triatmojo,

Qui,

El Sayed

et al. 2024

BuletinPeternak

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The farmers have encountered challenges in conducting livestock trade due to the absence of dealer activity caused by Anthrax and Foot Mouth Disease (FMD) epidemics. In this context, it is crucial to utilize technology in livestock marketing to obtain current market information from distant marketplaces and reduce the risk of contagion. To meet these purposes, a mobile phone application has been developed in order to be used by cattle farmers; after that, market testing has been conducted to gain feedback and determine the segmentation. Thus, the study aimed to examine the differences in the perceived ease of use, perceived usefulness, and social impact amongst farmers who are willing and unwilling to embrace a mobile phone application for digital marketing. A total of 968 cattle farmers were surveyed with stratified random sampling techniques in the Special Region of Yogyakarta. The data obtained were analyzed using mean difference inferential analysis. The result showed that farmers with various categories of age, education, farm revenue, farmers group, farmer experience, cattle ownership, and regions have significantly different (p<0.01) perceived usefulness (PU), perceived ease of use (PE), and social influence (SI) on mobile applications for livestock digital marketing. Furthermore, farmers willing to adopt mobile application have significantly higher (p<0.01) PU, PE, and SI factors. This study recommends mobile app developers evaluate potential user needs and background factors that may influence farmers' interest.

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“…The entrepreneurial landscape of farmers is shaped by a combination of factors such as government laws, socio-cultural norms, technological breakthroughs, market conditions, and financing availability. For example, market volatility can have a big impact on farmers' risk tolerance and investment choices, yet loan availability and supporting regulations might spur entrepreneurs to start their own businesses [11].…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing Farmers’ Entrepreneurial Behavior in Panchagarh, Bangladesh: An Integration of Semantic Differential Scale

Shahin,

Sarmin,

Sojib

et al. 2024

AJAEES

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The succession of activities to the exploitation of a new entrepreneurial opportunity is important for income diversification. The objective of this study was to determine the extent of entrepreneurial behaviour of the farmers as well as to determine its influential factors. The dependent variable was measured using one-to-seven-point semantic differential scale. In addition, seven levels of entrepreneurial behaviour were used in the scale. One hundred thirty-three (133) farmers were selected for data collection using a multi-stage random sampling procedure from selected six unions of Sadar upazila in Panchagarh district of Bangladesh. Descriptive statistics like percentages, means, standard deviations, indices, and rank order were employed to summarise the data gathered during the interviews. The inferential statistical investigation involved using correlation analysis, multiple regression analysis, stepwise regression analysis and path analysis using PLS-SEM (Partial Least Square-Structural Equation Model). The findings revealed that a significant proportion (85.7 percent) of the respondents held a repressive to optimistic entrepreneurial behaviour. The key factors influencing farmers’ entrepreneurial behaviour were educational qualification, extension contact, farm size, and age altogether accounting for 89.0% variance in the dependent variable. However, path analysis indicates that farmers’ educational qualification has the highest contribution on their entrepreneurial behaviour followed by farm size, extension media contact, and age. The farm size has substantial indirect effect through educational qualification and extension media contact on entrepreneurial behaviour. Well-designed interventions, particularly through educational activities and extension services are crucial for fostering farmers’ entrepreneurial engagement in agriculture and enhancing their success.

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Digital literacy and farmers’ entrepreneurial behavior—Empirical analysis based on CHFS2019 micro data

Cited by 10 publications

References 27 publications

Digital Literacy Level and Formal Credit Constraints: Probit Analysis of Farm Households’ Borrowing Behavior in China

Digital Literacy Level and Formal Credit Constraints: Probit Analysis of Farm Households’ Borrowing Behavior in China

Technological Acceptance of Cattle Farmers in Mobile Applications for Livestock Digital Marketing

Factors Influencing Farmers’ Entrepreneurial Behavior in Panchagarh, Bangladesh: An Integration of Semantic Differential Scale

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