A study on the long-term effects and mechanisms of internet information behavior on poverty alleviation among smallholder farmers: Evidence from China

Abstract

China has historically accomplished the task of eradicating absolute poverty, however, as a result of multiple external constraints and a lack of their own endogenous motivation, there is a general lack of viability among farmers who have been lifted out of poverty, with some of those who have been lifted out of poverty at risk of returning to poverty and marginalized populations at risk of becoming poor, and there are certain challenges to the longevity and stability of the poverty eradication of smallholder farmers. In the era of rapid development of information technology, the use of Internet information has become an important way to change the information asymmetry in rural areas, narrow the urban-rural digital divide and reduce the vulnerability of poverty at present. Based on this, this paper puts forward the corresponding research hypotheses on the theoretical basis of how Internet information behavior affects the long-term effects of poverty alleviation of smallholder farmers, and this paper is based on the empirical analysis of the household survey data of 240 smallholder farmers in 3 cities and 3 counties of H Province, in an attempt to explore empirical testing of the impact of Internet information behavior on the long-term effects of poverty alleviation of smallholder farmers, and to further reveal the intrinsic mechanism of the role of the internal mechanism of the transformation between the smallholder farmers' Internet information behavior and poverty alleviation of the long-term effects of poverty alleviation. This study found that (1) Internet information usage would be positively related to the long-term poverty alleviation of smallholder farmers; (2) Among the mechanisms of Internet information usage on the long-term poverty alleviation of smallholder farmers, agricultural income opportunities, employment opportunities and entrepreneurial business opportunities have significant mediating effects; (3) Formal social support and informal social support, all play a significant positive moderating role in the process of transformation of development opportunities carried out by smallholder farmers. The findings of the study have important practical implications for accelerating the poverty alleviation effect of the Internet and achieving sustainable poverty alleviation among small farmers to promote common prosperity.

1. Introduction

Poverty, as an inevitable and important issue related to economic development, is a major challenge faced by countries around the world, especially developing countries [1]. China used to be the developing country with the largest number of poor people in the world [2,3], China's poverty alleviation and development is an important component of global poverty alleviation efforts. Thanks to the implementation of the targeted poverty alleviation and eradication strategy since the 18th Party Congress, China has achieved a comprehensive victory in the battle against poverty and completed the arduous task of eliminating absolute poverty. Nevertheless, the challenge to completely eliminate poverty continues to be significant. Poverty in China still exists widely and continues to hinder sustainable development, undermining sustainable livelihoods [4,5]. China still has a long way to go to reduce poverty, based on the relevant statistics and GDP projection estimates in China's 2022 Economic Development Report (which judges as poor those below the typical upper-middle-income poverty line of $6.85 per person per day) [6], see Fig. 1(a) and (b). Smallholder farmers, the agricultural micro subjects that adopt the family-run production mode and use all kinds of specialty planting as the means of production, have a certain land area but are relatively backward in production and operation due to the bias of information acquisition. As a major component of the rural poor, smallholder farmers practice consumption-oriented subsistence farming [7,8] and depend primarily on agriculture for their livelihoods [9], with limited access to development opportunities, which also contributes to their lack of subsequent development capacity and vulnerability to relapse into poverty. According to relevant data, the average rate of poverty relapse in various parts of China currently stands at around 20%, and there are still 58.24 million low-income rural residents who are at high risk of returning to poverty [10]. With the evolution of major social conflicts, the focus of regional development in rural areas has shifted from bottom-line poverty alleviation to high-quality rural revitalization [[11], [12], [13]]. Therefore, in the post-poverty era, ensuring that small farmers can develop themselves sustainably, maintaining a good living condition and not falling into the poverty again is an urgent issue that needs to be addressed now, and it is also the focus of the National Rural Revitalization stage.

Fig. 1.

(a) Poverty rate, (b) Number of poor.

Note: Last grouped data available to calculate poverty is for 2019. Projections based on per capita GDP growth estimates, using a neutral distribution assumption with pass-through 0.85 to per capita household consumption.

Source: World Bank staff estimates using tabulated data from China's National Bureau of Statistics (NBS) and World Bank GDP growth projections (2023) [6].

Although previous studies have found that education, health, training, and government financial support have a positive effect on improving the human capital of smallholder farmers and thus effectively alleviating poverty, based on Sen's theory of viability, the root cause of poverty lies in the lack of, and deprivation of, the basic capabilities that should be possessed by individuals in the course of their development [14]. In order to realize the long-term sustainable poverty alleviation of smallholder farmers, it is necessary to focus on improving the self-development capacity of ordinary small farmers [15]. Along with the construction of Internet infrastructure and the popularization of smartphones, China has officially entered the era of digital economy. The popularization and promotion of the Internet has injected new vitality into rural development, see Fig. 2(a) and (b). The application of Internet technology has penetrated into all aspects of rural residents' production and life, changed the disadvantage of asymmetric information in rural areas to a certain extent [16], strengthened the endogenous momentum of the poor people's development, and brought about a profound impact on their daily behavior and decision-making. In the short term, the Internet can improve farmers' access to information and technology and improve agricultural production inputs, and in the long term, it can improve farmers' agricultural management capacity [17,18]. The Internet provides more employment information for small farmers, who can access rich, timely and accurate information anytime and anywhere, creating a more equitable employment environment [19]. The embedding of the Internet enhances farmers' ability to access and develop market information, shows them diversified ways and means of off-farm employment [20], and enables them to become more proficient in modern technology and equipment, improve their work efficiency [21], and improve their family's economic situation, thus contributing to the alleviation of family poverty [22,23]. Therefore, in the context of the reality of increasing Internet penetration in rural areas, it is of great practical significance to explore the impact and mechanism of Internet information behavior on the long-term effect of poverty alleviation of small farmers, and to innovate the path of poverty alleviation of small farmers in the long term.

Fig. 2.

(a) The number of internet users and the internet penetration rate, (b) Internet penetration rate in urban and rural areas in China.

Source: The 50th Statistical Report on China's Internet Development (2022)

Based on this, this paper will explore the relationship between the use of Internet information and the long-term poverty alleviation of small farmers by means of empirical analysis, and clarify the path mechanism between the use of Internet information and the long-term poverty alleviation of small farmers. Starting from development opportunities, it will consider what kind of development opportunities will be brought by small farmers through the use of Internet information under the external social environment, so as to grasp the opportunities, improve the endogenous motivation of small farmers' development, and promote the effective transformation of the opportunities into economic income and realize personal development.

2. Literature review

2.1. Methods and means of poverty alleviation

Poverty is a complex socio-economic phenomenon that evolves with changes in the dominant development paradigm [24]. Poverty not only reflects incompetence and helplessness but also represents a complex process that affects social and national policies [25]. Poverty is a challenge faced by all countries. Eliminating poverty is an ideal constantly pursued by human beings, and human society has never stopped alleviating or eradicating poverty [26]. In the past few decades, both the government and academic circles have carried out a considerable amount of poverty alleviation work and conducted extensive discussions on various poverty alleviation methods, including policy-driven poverty alleviation [27], industry-based poverty alleviation [28], education-based poverty alleviation [29], financial poverty alleviation [30], photovoltaic poverty alleviation [31], etc.

The government has launched a series of policies and measures to promote poverty governance in terms of top-level design, institutional arrangements, and typical practices, thus enhancing the endogenous development capacity of poor people and poor regions [27,32]. Pro-poor resettlement (PAR), a national rural development policy, uses resettlement as a tool to address environmental and poverty-related issues [33]; As an important measure of targeted poverty alleviation, the relocation of poverty-stricken populations should be based on thorough scientific research and planning to fully respect the wishes of farmers [34]. In addition, land resources are a cross-cutting contributor to poverty alleviation [35], due to the fact that rural poverty is closely related to land resource endowment, quality, and utilization, and that innovative land engineering and policies contribute to rural poverty alleviation through land and financial support [32,35]; The development of forestry projects is not only beneficial for improving the environment but also an important way to alleviate poverty during the poverty alleviation process [36]. Many countries have implemented ecological measures through forestry development to alleviate poverty and promote economic development. Through their research, as part of rural economies, forestry activity can increase rural residents' income and reduce poverty [37]. Since 2014, the Chinese government has begun implementing photovoltaic power generation as a means of poverty alleviation, which not only conforms to the concept of green development but also accelerates poverty alleviation in rural areas [38]. Photovoltaic power generation can not only create income for poor families and communities but also achieve poverty alleviation and low-carbon development [39]. Studies have shown that financial development can help alleviate poverty, and the role of financial poverty alleviation is both direct and indirect [40]. Financial development mainly uses direct methods such as improving the income level of the poor, reducing income inequality, and expanding the coverage of financial services to reduce poverty [30], while indirectly reducing poverty through promoting capital accumulation, stimulating technological innovation, and driving economic growth [41].

2.2. The internet information usage and poverty alleviation

The rapid development of internet technology has provided new ideas for poverty alleviation in impoverished areas of China and accelerated the pace of poverty reduction [42]. In the context of the growing popularity and rapid development of the Internet, information technology facilitates the production, dissemination, and consumption of rural goods, and Internet access can be effective in reducing poverty levels [43]. In the context of growing Internet use and rapid development in China, communication technologies have facilitated the generation, dissemination and consumption of rural goods.

Using Internet information can effectively improve smallholder farmers' access to financial and agricultural services [44], increase the accessibility of their market inputs and outputs [45], and increase their participation in non-farm and other income-generating opportunities by connecting them to the outside world through Internet information [46]. The use of the Internet can help reduce rural information asymmetries [47]. Using the Internet, farmers have access to more effective and timely agricultural information on the quantity and price of agricultural inputs (e.g., land, seeds, pesticides, fertilizers, etc.), technical production services (pest control, soil testing, vegetation preservation, etc.), and agricultural markets and policies [47], which enables them to make informed decisions, optimize resource use, and increase productivity [47,48]. Mobile electronic devices such as cell phones, as carriers of ICTs, are commonly used as tools for information production and dissemination, and to some extent facilitate the social mobility and status of low-income people [49]. The convenience, low cost and ease of use of cell phones greatly facilitates access to and utilization of information by smallholder farmers and increases agricultural productivity, thereby increasing agricultural production and reducing poverty [50]. By harnessing the power of the Internet, farmers have access to real-time data, market information, weather forecasts, and precision farming technologies [44]. These resources enable them to make informed decisions, optimize resource use, increase productivity [48], and ultimately contribute to rural poverty reduction [46].

By combing through the literature, it can be found that there has been a relatively deep accumulation of poverty governance strategies, governance methods and the impact of Internet information on poverty, which have laid the foundation for the research on poverty reduction of Internet information. By summarizing the existing literature, we find that: (1) existing poverty alleviation measures, including government policies to alleviate poverty, industrial poverty alleviation, education poverty alleviation, and poverty alleviation with the help of the Internet, mainly consider the effect of reducing poverty and increasing income. However, the long-term nature of poverty alleviation and poverty reduction are very different in meaning, and the effect of poverty alleviation cannot be considered only from the phenomenon level. (2) In terms of the mechanism of action, the existing studies mostly consider from the perspective of livelihood capital, and consider the impact of social capital, human capital, etc. on the accumulation of livelihood capital of farm households. They ignore the fact that the lack of smallholder farmers' own feasible ability is the essential factor for the long-term existence of poverty, and at the same time they do not take into account the influence of external environmental factors in it, and they lack a deeper level of excavation.

Based on the above analysis, this paper mainly proposes innovations in terms of research perspectives, research theories and research contents (1) In terms of research perspective, different from the previous research on the effect of Internet information on reducing poverty and increasing income, the study focuses on the long-term problem of poverty alleviation in the post-poverty era, which further broadens the perspective of poverty research. (2) In terms of research theory, based on the theory of feasible ability, the study addresses the problem of unstable poverty alleviation and the ease of returning to poverty of small-scale farmers from the perspective of the endogenous energy of the micro-individuals. (3) In terms of the content of the study, the relationship between the information-using behavior of small farmers and the long-term stability of poverty eradication has been established, and its internal mechanism has been clarified by means of empirical research. Considering internal and external factors comprehensively, starting from small farmers' own development opportunities and external social support, we explore the role mechanism between Internet information behavior on the long-term poverty alleviation of smallholder farmers.

3. Conceptual models and hypotheses

3.1. Internet information usage and the long-term effects

There is agreement in the development literature that economic growth is the strongest driver of long-term poverty reduction [51,52]. Economic development is the basis for income growth for poor groups, substantial economic growth benefits poor groups, and countries with higher rates of economic growth are able to achieve greater poverty reduction [53]. ICT applications in developing countries are often part of an overall strategy for economic growth that relies on a trickle-down effect on the poor [54]. The development and access to social networks through low-cost ICTs, telecentres will enhance timely access to accurate and reliable information by the poor [55]. Investments in telecommunications have long been credited with boosting productivity and economic growth [[56], [57], [58]]. In the era of Internet plus, we should gradually explore new poverty alleviation strategies for economic and social development in poverty-stricken areas and innovate poverty alleviation concepts, policies, and methods. The following hypothesis is posited.

H1

Internet information usage has a positive feedback effect on the long-term effect of smallholder farmers out of poverty.

3.2. Mediating effect of development opportunities

Primarily, according to Amartya Sen's rights-based poverty theory [14,59], the representations of poverty exhibited by the poor are not necessarily determined by their own endowments; the theory suggests that lack of opportunity is also an important reason why small farmers are unable to increase their livelihood capital. If farmers are provided with more development opportunities, they can enhance their livelihood capital and thus reduce their multidimensional poverty [60]. Most scholars believe that Internet information plays an important role in the poverty reduction effect, mainly through mediating mechanisms such as improving agricultural production efficiency, increasing employment opportunities and increasing income.

Agricultural informatics, which combines the advancement of agricultural information, agricultural development and entrepreneurship, is a field that provides agricultural services through information and communication technologies and the Internet [61]. Any ICT intervention that improves the livelihoods of poor rural farmers will have significant direct and indirect impacts on enhancing agricultural production, marketing and post-harvest activities, which in turn can contribute to poverty reduction [62]. E-agriculture focuses on promoting agricultural and rural development through improved information and communication processes [63], which in turn helps to reduce poverty. Poor areas in rural China generally have an abundant working-age population but lack employment opportunities [64]. Internet usage in rural areas, especially in remote rural areas, promoted the transfer of surplus rural labor [65]. The digital economy brings farmers more employment options and improves the allocation of market resources [66]. The advantage of information resources reduces the gap between urban and rural areas and promotes the economic development of the village [67]. ICTs will not only empower the gender but sustain poverty alleviation programmes which in time past have failed in Nigeria through provision of new and enhanced opportunities [55]. Further regression analysis suggests that poverty can be addressed through economic growth and job creation [68]. Scholars have argued that financial opportunities, information opportunities, etc. are beneficial for farm households to escape from poverty [69]. Based on the definition of opportunities by scholars in poverty research [[70], [71], [72]], the development opportunities were classified into four areas: agricultural income opportunities, employment opportunities for labor, entrepreneurial business opportunities, and financial credit opportunities, taking into account the livelihood options of farm households. This led to the research hypothesis that.

H2

Development opportunities have a mediating role between smallholder Internet information usage and the long-term nature of poverty eradication.

H2a

Agricultural income opportunities mediate between smallholder Internet information usage and long-term poverty alleviation.

H2b

Employment opportunities mediated between smallholder Internet information usage and long-term poverty alleviation.

H2c

Entrepreneurial business opportunities mediate between smallholder Internet information usage and long-term poverty alleviation.

H2d

Financial credit opportunities mediated between smallholder Internet information usage and long-term poverty alleviation.

3.3. Moderating effect of social support

According to the “Lewin Model of Behavior” proposed by American social psychologist Kurt Lewin [73], human behavior is the product of the interaction between individuals and their environment, and the way, direction and intensity of human behavior are mainly influenced and regulated by the internal factors of individuals and external environmental factors. Social support is a way to provide social provision and protection for people in many social forms, and this social provision can be either psychological support and guidance or life support and provision [74]. Most of the existing studies divide social support into formal and informal social support [[75], [76], [77]]. Formal social support is mostly provided to assistance recipients through a “top-down” approach, while informal social support is an emotional and unpaid support system formed by people based on blood and geographical relationships [78].

The process by which farmers gain development opportunities from Internet information use and thus achieve the long-term effects of poverty alleviation is not only related to individual traits but also influenced by the external environment. Social support, as the most significant external influence on individual behavior, is an important external force for farmers to translate their identified development opportunities into concrete effectiveness. Scholars have studied whether the poor can expect more social support [79] and the relationship between household poverty and social support [80]. By focusing on the negotiation processes involved in the creation, maintenance, and mobilization of social relationships, our understanding of the complex and dynamic role that social support networks play in the daily lives of working mothers living in poverty is facilitated [81]. Social support, in turn, includes both formal and informal organizational aspects. Based on the above theoretical analysis, this paper incorporates Internet information use, development opportunities, social support, and long-term poverty alleviation into a unified framework to test the moderating role of social support. As a result, the following research hypothesis is proposed.

H3

Formal social supporting plays a moderating role between smallholder development opportunities and long-term effect of poverty alleviation.

H4

Informal social supporting plays a moderating role between smallholder development opportunities and long-term effect of poverty alleviation.

Based on the above proposed hypothesis, the research hypotheses formulation of this paper is established, as shown in Fig. 3.

Fig. 3.

Conceptual models.

4. Methodology

4.1. Research design

Combined with the above analysis, the research design process for the empirical analysis is as follows:

• (1)Explain the indicators involved in this part, determine the corresponding characterization variables according to the literature base and the actual situation, and quantitatively assign values to the results of variables selection, so as to facilitate the regression analysis.
• (2)Design the questionnaire with the indicators, quantify the questionnaire, obtain the research data through field research, and carry out descriptive statistics, and initially observe the basic characteristics of the data obtained.
• (3)Design the corresponding econometric model by combining the relevant literature and the actual situation.
• (4)Conduct reliability and validity tests on the collected data to check the validity of the questionnaire and sample
• (5)Use the econometric model to explore the relationship between smallholder Internet information behavior and stable poverty alleviation, make corresponding analysis with regression results, and then use the instrumental variable method to conduct endogeneity suggestion and robustness test on the baseline model.
• (6)On the basis of the impact effect model, further reveal the inner mechanism of the transformation between smallholder farmers' Internet information behavior and stable poverty alleviation, explore the path and boundary conditions of Internet information use on smallholder farmers' stable poverty alleviation, and find out the mediating and regulating variables.

4.2. Variable selection

• (1)Dependent variable: Long-term effect of poverty alleviation

Under the impetus of the policy of precise poverty eradication, in 2020, China has already achieved the goal of poverty eradication, eliminated absolute poverty, and realized that the entire population does not worry about food and clothing, with compulsory education, basic medical care, and housing security guaranteed [82], therefore, objectively there are already no absolute poor households, but due to the existence of smallholder farmers in China's rural areas of the risky nature of agricultural production, the lack of endogenous motivation, and the instability of income and other problems, smallholder poverty alleviation whether to realize the long-term become in the post-poverty era need to focus on the problem. In the existing literature, poverty alleviation among smallholder farmers is usually measured by their economic status [83,84]. Since the revenue sustainability of poor households is more advantageous than poverty measurement from income or consumption in smoothing out income fluctuations, protecting against the risk of uncertainty and predicting future welfare levels [85]. In this paper, we expand the dimension of economic status to include revenue sustainability in order to explore the long-term sustainability of poverty alleviation. For the dimension of economic status, two indicators are commonly used to characterize annual household income per capita and income and expenditure [86], while for income sustainability, we mainly use two indicators to characterize the ability and willingness to increase income.

• (2)Core Independent variable: Internet information behavior

The information behavior mentioned in this paper refers to the behavior of converting the information obtained from the Internet into productivity and using the information reasonably and effectively to create value, and the Internet information behavior defined in this study based on the micro perspective includes the frequency of using Internet information, the source preference of using Internet information and the scope of using Internet information for small farmers. For the micro subject small farmers, due to their own insufficient resource endowment and feasible ability, we mainly divide the source preference of Internet information use into two channels: cell phone Internet access and computer Internet access. Access to information through the convenience and low cost of mobile electronic devices, such as cell phones and computers, improves the ability of households to access livelihood assets and overcome their vulnerability to a certain extent [87], thus contributing to the improvement of rural livelihoods and the reduction of poverty [88]. The scope of Internet information use represents the specific application contexts of smallholder farmers in the use of Internet information. Using mobile communications such as cell phones, farmers can use Internet information to achieve from simple communication to financial transactions [89], and enhance their development capacity through continuous learning to improve their knowledge level [90]. Based on this, the scope of Internet information use is divided into the use of Internet information for learning and work, social communication and leisure.

• (3)Control variables

The control variables in this paper include farm household level and head of household level, with gender, age, education level, and marital status at the head of household level; and household size and ever being a poor household at the household level. Generally speaking, male heads of household are more open and flexible in information utilization than female heads of household, with higher access to market information, making it easier to achieve stability in poverty reduction [91]; age and health are the most direct manifestations of human capital, and these are factors that have an impact on the stability of farm households in poverty alleviation, with the older the household, the less receptive it is to Internet information; the higher the number of years of education a farm household receives, the greater its tendency to learn new knowledge and use new technology will be greater; the family level has the family size and whether it has ever been a poor household. Household size is the number of household members, which reflects future earning capacity but is also related to the structure of household members [92]. Whether or not a household has ever been poor represents its previous economic base, which also has a direct impact on the stability of a farmer's escape from poverty.

The specific variable index selection, definition and assignment are shown in Table 1.

Table 1.

Indicator selection, variable definition and assignment.

Variable Category	Indicator Selection	Variable Selection	Indicator assignment
Dependent variables	Economic Status	Annual household income per capita	10000 RMB and below = 1; 10000–20000 RMB = 2; 20000–30000 RMB = 3; 30000–40000 RMB = 4; 50000 RMB and above = 5
		Income and Expenditure	Income is much greater than expenses = 5; Income is greater than expenses = 4,; Income is slightly greater than = 3; Break even = 2; Not enough income to cover expenses = 1
	Revenue Sustainability	Income-generating capacity	Type of work performed: simple manual labor = 1; heavy manual labor = 2; easy to start technical work = 3; complex professional and technical work = 4; business management = 5
		Willingness to increase income	Total months of primary work performed in the last 12 months: 2 months or less = 1; 2–4 months = 2; 4–7 months = 3; 7–10 months = 4; 10–12 months = 5
Independent variables	Density of information utilization	Frequency of Internet information usage	Very frequently (almost every day) = 5; often (2–3 times a week) = 4; sometimes (2–3 times a month) = 3; rarely (once every few months) = 2; never (never) = 1
	Information source preferences	Information Access	Preference of using cell phone: 7 days a week = 5; 5–7 days a week = 4; 3–5 days a week = 3; 1–3 days a week = 2; not using = 1 Preference for computer use: 7 days a week = 5; 5–7 days a week = 4; 3–5 days a week = 3; 1–3 days a week = 2; no use = 1
	Scope of information use	Study Work	Very frequently (almost every day) = 5; often (2–3 times a week) = 4; sometimes (2–3 times a month) = 3; rarely (once every few months) = 2; never (never) = 1
		Social Communication	Very frequently (almost every day) = 5; often (2–3 times a week) = 4; sometimes (2–3 times a month) = 3; rarely (once every few months) = 2; never (never) = 1
		Leisure & Entertainment	Very frequently (almost every day) = 5; often (2–3 times a week) = 4; sometimes (2–3 times a month) = 3; rarely (once every few months) = 2; never (never) = 1
Control variables	Householder characteristics	Age	Real age
		Gender	Female = 0, Male = 1
		Education level	Illiterate/semi-literate = 1, junior high school and below = 2, high school/junior college/technical school/vocational high school = 3, college = 4, university undergraduate and above = 5
		Health Status	Disability or major illness = 1, mild disability or chronic illness = 2, some minor ailments but not major problems = 3, no physical problems = 4, very good physical condition = 5
		Marital Status	In marriage = 1, other = 0
	Family Features	Family size	Actual number of family members
		Ever been a poor household	Yes = 0, No = 1

Note: RMB indeed stands for Renminbi, which is the official currency of the People's Republic of China.

4.3. Data collection and sample profile

As the Internet information behavior of small farmers is characterized by strong privacy and individual differences, the existing statistical data are not specifically for small farmers, much less specifically for the Internet information behavior, which cannot meet the needs of this study in terms of both sample selection and data coverage, so this study intends to conduct a questionnaire design, combined with the theme of this part of the study, i.e., whether the Internet information behavior can produce a long-term effect of poverty alleviation, to design a highly targeted questionnaire. In order to ensure the authenticity and reliability of the data obtained, it is proposed to use a field study in the households to produce authentic data through in-depth communication with the smallholder farmers. The researcher obtained permission from the head of the institute and the study area to comply with ethical rules. Farmers participating in the study were informed about the confidentiality and anonymity of the questionnaire. The participants voluntarily gave their consent and participated in the study.

The data used in this paper are selected from a questionnaire survey of farm households conducted by the group in October–November 2021 in H Province, central China. As the main grain-producing area in China, H Province has long been suffering from the real problems of scattered operations, low incomes, slow growth, and many bottlenecks, which is an important microcosm of China's rural problems and is significantly representative [93]. Therefore, the selection of H Province as the sample source will make the study more targeted and its conclusions and recommendations more generalized. The research team randomly selected L County in the east, B County in the central and west, and L County in the northeast as survey sites. Each survey site was randomly stratified according to the level of economic development in each county to select 2 to 3 representative townships, each sample township stratified to select one to two sample villages (Fig. 4), and each sample village randomly selected 20–30 sample small farmers, and research members conducted questionnaire surveys through one-on-one household interviews. Finally, questionnaires were randomly distributed in 7 townships and 9 sample villages, with a total of 263 households surveyed, excluding outliers and invalid questionnaires, and finally 240 valid questionnaires were obtained, with an efficiency rate of 91.25%.The demographic information of the participants shown in Table 2.

Fig. 4.

Data source.

Table 2.

Statistical description of sample individual characteristics.

Variables	Options	Number of people	Percentage
Age	Under 20 years old	12	5.00
	20–35 years old	42	17.50
	35–50 years old	97	40.42
	50–65 years old	68	28.33
	Over 65 years old (elderly)	21	8.75
Gender	Female	108	45.00
	Male	132	55.00
Education	Illiterate	4	1.67
	Junior high school and below	189	78.75
	High school/junior high school/technical school/vocational high school	26	10.83
	College	15	6.25
	Undergraduate and above	6	2.50
Health Status	Disability or major illness	8	3.33
	Mild disability or chronic illness	22	9.17
	Some minor problems, but not major	122	50.83
	No physical problems	50	20.84
	In very good physical condition	38	15.83
Marital Status	In marriage	157	65.41
	Other	83	34.59
Family size	8	13	5.41
	7	84	35.00
	6	76	31.67
	5	57	23.75
	4	10	4.17
Ever been a poor household	Yes	201	83.75
	No	39	16.25

The statistical results of the sample are as follows. In terms of the age distribution of the sample, the largest number of small farmers were aged 35–50 years old, with 97 people, accounting for 40.42%; followed by farmers aged 50–65 years old, with 68 people, accounting for 28.33%; then farmers aged 25–35 years old, with 42 people, accounting for 17.50%; those aged under 20 years old and over 65 years old accounted for 5.00% and 8.75%, respectively. This indicates that the rural areas are dominated by middle-aged and older groups, with fewer young people. From the gender distribution of the sample, 132 males (55%) and 108 females (45%) were found. This reflects the fact that the head of household or the main decision maker in rural households is predominantly male. From the distribution of education level of the sample, the number of junior high school and below is the largest, 189 people, accounting for 78.8% of the total sample size, followed by 26 farmers in high school/junior high school/technical school/vocational high school, accounting for 10.8% of the total sample size. This is basically consistent with the distribution of the real education level in rural areas in China, with about 80% of the sample farmers having an education level of junior high school, and the popularization of nine-year compulsory education has enabled farmers to receive good primary and secondary education, but the proportion of farmers able to obtain higher education is still low, and it also reflects that the proportion of new-generation young people who have obtained high education and have returned to their hometowns is relatively low, and that they are more inclined to choose to take up employment in the cities; From the distribution of health status of the sample, the total number of farmers with some minor problems, but not big problems, no health problems and very good health is 210, accounting for 87.50%; 22 farmers with self-assessed mild disabilities or chronic diseases, accounting for 9.17%; 8 farmers with self-assessed disabilities or major diseases, accounting for 3.33%. It is clear that most farmers are in relatively good health, and a healthy body is the most basic guarantee, whether they are working in agriculture or going out to work. About 65.41% of the survey sample were in a marital status, while about 34.59% were not. The survey sample had a diverse distribution of family sizes, with 7-member families and 6-member families being the most common. The number of larger family sizes (8-person families) was low in this sample. The experience of poverty is common in the sample, with 83.75% of the sample having been in poverty at one time or another.

4.4. Model specification

4.4.1. Baseline model

This paper first uses a multiple regression model to analyze the impact of smallholder Internet information use on long-term effect of poverty alleviation. Based on the previous analysis, the baseline econometric model is derived as follows.

$${SOPA}_i={\gamma }_0+{\gamma }_{1}ICT+\lambda X_i+{\epsilon }_1$$ (1)

in equation (1), ${SOPA}_i$ is the dependent variable, representing the long-term effect of small-farmers out of poverty; $ICT$ is the independent variable, representing the behavior of Internet information usage, ${\gamma }_0$ and ${\gamma }_1$ represent the regression coefficients, $X_i$ represents all control variables, and ${\epsilon }_1$ represents the random disturbance term.

4.4.2. Mediated effects model

Based on the theoretical analysis in the previous section, this section further tests the relationship between development opportunities in smallholder Internet information use and the long-term effect of poverty alleviation. This paper uses stepwise regression to test the mediating effect of development opportunities between family capital and offspring socioeconomic status, using the design of the mediating effect model by existing literature [94,95] to obtain the following econometric model.

$${DO}_i={\sigma }_0+{\sigma }_{1}ICT+\lambda X_i+{\epsilon }_2$$ (2)

$${SOPA}_i={\beta }_0+{\beta }_1{DO}_i+\lambda X_i+{\epsilon }_3$$ (3)

$${SOPA}_i={\theta }_0+{\theta }_{1}ICT+{\theta }_2{DO}_i+\lambda X_i+{\epsilon }_4$$ (4)

The test procedure and determination is carried out in accordance with the following steps. First, test the impact of Internet information use on development opportunities in equation (2). Second, to test the effect of development opportunities in equation (3) on the long-term effect of poverty alleviation, if Internet information usage affects long-term effect of poverty alleviation through development opportunities, then the coefficient ${\beta }_1$ and ${\sigma }_1$ should be significant and ${\gamma }_1$ is consistent with the direction. Finally, to test whether the mediating effect of equation (4) development opportunity is complete, i.e., after controlling for farmers' development opportunity, if there is both a direct effect of Internet information use on the long-term effect of poverty alleviation and an indirect effect by affecting development opportunity and thus long-term out of poverty, both ${\theta }_1$ and ${\theta }_2$ should pass the significance test and the mediating effect exists.

4.4.3. Moderating effects models

Based on the previous research hypothesis, a three-step test of moderating effects was used to test the moderating effect of formal support using the interaction term of the variables, incorporating development opportunities, formal support, the long-term effect of poverty alleviation, and the interaction term and each control variable of this study in turn, on top of the centralization of each variable. Through the existing literature on the moderation effect model [96], the moderation effect test model of this study is as follows

$${SOPA}_i={\phi }_0+{\phi }_1{DO}_i+\lambda X_i+{\epsilon }_5$$ (5)

$${SOPA}_i={\mu }_0+{\mu }_1{SS}_i+{\mu }_2{DO}_i+\lambda X_i+{\epsilon }_6$$ (6)

$${SOPA}_i={\nu }_0+{\nu }_1{SS}_i+{\nu }_2{DO}_i+{\nu }_3{SS}_i*{DO}_i+\lambda X_i+{\epsilon }_7$$ (7)

in these formulations, ${SOPA}_i$ is the explanatory variable representing the long-term effect of poverty alleviation, ${DO}_i$ is the explanatory variable representing the development opportunities of smallholder farmers, ${SS}_i$ represents social support, $X_i$ represents all control variable, and the $\mathrm{\epsilon }$ represents the random disturbance term. The moderating effects testing procedure follows the following three steps. First, without considering the moderating variables, equation (5) is used to analyze the impact of development opportunities on the longevity of poverty eradication. Second, based on the first step, the moderating variable social support is introduced using equation (6). Third, based on the second step, the interaction term between development opportunities and social support is introduced in equation (7). If the interaction shows significance, it means that there is a moderating effect.

4.5. Reliability and validity analysis

In this paper, STATA 15.0 was used to test the reliability of the questionnaire as a whole and each question item, and Cronbach's alpha was used to measure the level of reliability. In general, a larger Cronbach's Alpha value indicates better correlation between the question terms, with $\mathrm{\alpha }<0.6$ indicating poor reliability, $0.6<\mathrm{\alpha }<0.8$ indicating a good level of reliability, and $\mathrm{\alpha }>0.8$ indicating very good reliability. Through the coefficient test, the Cronbach's alpha for each dimension of the questionnaire designed in this study was above 0.8, which indicates that the questionnaire used in this study has a high reliability. The results are shown in Table 3.

Table 3.

Reliability test results.

Variables	Title items	Corrected items Total correlation	Cronbach's Alpha
Long-term effects of poverty alleviation	Annual household income per capita	0.777	0.840
	Income and expenses for the past year	0.775
	Income-generating capacity	0.808
	Willingness to increase income	0.791
Internet Information Usage	Information utilization density	0.786	0.823
	Information source preferences	0.798
	Frequency of using the Internet to learn to work	0.758
	Frequency of social communication using the Internet	0.794
	Frequency of using Internet for leisure and entertainment	0.803

In the validity test, the test data were analyzed using Bartlett's sphericity test (KMO) values. The closer the KMO value and 1, the stronger the correlation between the variables; in general, as long as the KMO value is greater than 0.6 and the p-value is less than 0.05, it can prove that the validity of the questionnaire meets the standard and passes the validity test. According to the results of STATA calculation, the survey questions in this study passed the validity test, and the validity of the questionnaire was good, and the KMO values of all dimensional variables were greater than 0.8 and the p-values were less than 0.01. The KMO values and p-values of each dimensional variable are shown in Table 4.

Table 4.

Validity test results.

Variables	KMO value	Approximate cardinality	Degree of freedom	Significance
Long-term effects of poverty alleviation	0.806	368.845	6	0.000
Information use behavior	0.823	421.420	10	0.000

5. Results and discussions

5.1. Results of the baseline regression

The independent variables can be divided into three indicators such as frequency of Internet information use, Internet information source preference, and Internet use range, and a method is needed to synthesize these three indicators into a composite indicator. In this paper, mainly referring to the objective assignment method, these three indicators are processed by the entropy value method [97,98], and finally a comprehensive indicator Internet information usage behavior, namely $ICT$, can be obtained.

The relationship between smallholder farmers' Internet information use behavior and long-term effect of poverty alleviation was then explored using the baseline econometric model, and the empirical results are presented in Table 5. Column (1) shows the results of the regression with the addition of control variables only, and column (2) shows the results of the regression with the addition of variables related to the characteristics of the household head and the characteristics of the farm household, followed by the addition of the independent variable Internet information use behavior. When only the control variables were added, the model fit superiority was 0.114, and when the independent variables were added, the model fit superiority changed to 0.370, indicating that the model with the inclusion of the independent variables had a better fit. According to the regression results, the regression coefficient of Internet information use behavior is 0.512 and positively affects the long-term effect of farmers' poverty alleviation at 1% significance level, which indicates that the more frequent the use of Internet information, the longer the effect of farmers' escape from poverty.

Table 5.

Multiple regression results of Internet information behavior on long-term effect of poverty alleviation.

Variables	（1）	（2）
Age	−0.152***	−.070***
Gender	0.004	0.009
Years of education	0.237***	0.185***
Health status assignment	0.079**	0.093**
Marital status assignment	0.031*	0.089*
Family Size	0.136	0.106
Ever been a poor household	−0.032*	−0.058*
Internet information usage behavior		0.512***
R2	0.140	0.391
Adjusting R2	0.114	0.370
F-value	5.396***	18.553***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

According to the empirical results, the individual characteristics of farmers also play an important role in the longevity of poverty alleviation. The age of smallholder farmers is significantly and negatively related to the longevity of poverty exit, and most rural smallholder farmers are teenagers or older farmers with lower physical labor and resource endowment, and rarely have the ability to achieve high income to achieve the longevity of poverty exit. The number of years of education of smallholder farmers significantly positively affects the time out of poverty, and farmers with high apparent education are more capable of achieving long-term poverty reduction. The health status of smallholder farmers positively affects the long-term exit from poverty at the 5% statistical level, and farmers who are healthy have a longer exit from poverty compared to those who are unhealthy. In general, years of education and health status, as the most central components of human capital, directly affect the income level of farm households, which in turn affects the longevity of their exit from poverty.

5.2. Endogeneity and robustness test

5.2.1. Endogeneity test

Firstly, considering that there may be omitted variables that affect both Internet information use and long-term effect of poverty alleviation, and secondly, considering that there is an inverse causal relationship between Internet information use and long-term effect of poverty alleviation of smallholder farmers, the more likely smallholder farmers with better economic conditions may use Internet information. In order to overcome the estimation bias caused by endogeneity and omitted variables, this paper refers to the existing literature, using instrumental variable method [99], using the importance of the Internet as an information channel by household heads as an instrumental variable for Internet information use, which satisfies the correlation and exogeneity conditions. First, the higher the importance of the Internet as an information channel for household heads, the more likely they are to use the Internet, i.e., this variable is positively correlated with the probability of Internet use; second, the importance of the Internet is not directly related to the long-term effect of farmers' poverty alleviation, thus satisfying the exogeneity condition.

After including the importance of the Internet as an information channel as an explanatory variable, the regression results are shown in Table 6 with a regression coefficient of 0.591 and significant at the level of 1, indicating that Internet information use does promote increased long-term effect in poverty alleviation among smallholder farmers, hypotheses H1 is supported.

Table 6.

Endogenous treatment results.

Variables	Direct effects Model 1
Age	−0.135*
Gender	0.012
Years of education	0.088
Health status assignment	0.069
Marital status assignment	−0.003
Family Size	0.104*
Ever been a poor household	−0.058
The importance of the Internet as an information channel	0.591***
R2	0.481
Adjusting R2	0.461
F-value	23.715***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

5.2.2. Robustness test

With the gradual popularization of mobile communication devices, mobile communication tools such as cell phones and computers provide the hardware basis for people to access the Internet at any time, and it is also more convenient and faster to use the Internet for information acquisition and communication traffic, and at the same time, the use of Internet information channels to obtain information can create a great deal of dependency. Therefore, “Internet information dependence” was used as a replacement variable for Internet information use for robustness testing, and the parameter estimation results of Internet information dependence on the long-term effect of farmers' poverty alleviation are shown in Table 7, with a regression coefficient of 0.523, which positively affects the long-term effect of farmers' poverty alleviation at the 1% significance level, indicating that the model of the effect of Internet information use on the long-term effect of poverty alleviation is robust.

Table 7.

Direct effect robustness test.

Variables	Direct effects
	Model 1
Age	−0.065*
Gender	0.043
Years of education	0.011**
Health status assignment	0.054
Marital status assignment	−0.073
Family Size	0.1084*
Ever been a poor household	−0.058
Network information dependence	0.523***
R2	0.457
Adjusting R2	0.452
F-value	23.635***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

5.3. Effects of mediating model

5.3.1. Mediating effects

The empirical results are shown in Table 8. After accounting for both control, independent and mediating variables, the goodness-of-fit of the mediated model is 0.570, indicating that the model with the inclusion of mediating variables fits better than the direct effect. According to the regression results in the table, Internet information use has a significant positive effect on development opportunities with a coefficient of 0.594; development opportunities have a significant positive effect on their long-term effect of poverty alleviation with a coefficient of 0.594; after adding both Internet information use and development opportunities, the regression coefficient of Internet information use is 0.337 and that of development opportunities is 0.433, and both are significantly positive. This suggests that Internet information use plays an imperfect mediating role by affecting development opportunities and thus the long-term effect of poverty eradication, and hypothesis H2 is supported.

Table 8.

Regression results of the mediation effect test of development opportunities.

Variables	Development Opportunities		Long-term effect of poverty alleviation
	Model 2		Model 3		Model 4
Age	−0.120*	−0.068*	−0.152***	−0.135*	−0.105*
Gender	0.102	0.097	0.004	0.012	0.054
Years of education	0.220**	0.101**	0.237***	0.088**	0.045**
Health status assignment	0.001	−0.001	0.079**	0.069	0.069*
Marital status assignment	0.014*	0.005*	0.031*	0.003*	0.001*
Family Size	0.011	−0.024	0.136	0.104*	0.114*
Ever been a poor household	−0.006	−0.031	−0.032*	−0.058	−0.045
Internet information usage behavior		0.594***			0.337***
Development Opportunities				0.594***	0.433*
R2	0.102	0.433	0.140	0.481	0.588
Adjusting R2	0.071	0.41	0.114	0.461	0.570
F-value	3.290**	19.481***	5.396***	23.715***	32.649***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

5.3.2. Heterogeneity test

The results of the empirical analysis presented above show that development opportunities mediate the relationship between Internet information use and the long-term effect of smallholder farmers out of poverty. However, it is not clear what development opportunities Internet information use is more conducive to bring to smallholder farmers to achieve long-term poverty alleviation. Next, we specifically discuss the heterogeneity of agricultural development opportunities, employment opportunities, entrepreneurial business opportunities, and financial credit opportunities that play a mediating effect between Internet information use and the long-term effect of smallholder farmers' poverty alleviation. Following the test steps and determination procedures of the intermediary effects in the previous section, the intermediary effects of agricultural income opportunities, labor employment opportunities, entrepreneurial business opportunities, and financial credit opportunities are tested empirically, respectively.

The results of the test for mediating effects of agricultural income generation opportunities are shown in Table 9. The regression results indicate that agricultural income opportunities mediate the relationship between smallholder Internet information use and long-term effect of poverty alleviation, and hypothesis H2a holds. It shows that the agricultural income enhancement opportunities brought by the use of Internet information to farmers largely enhance the economic income and personal development of smallholder farmers.

Table 9.

The mediating effect test of the opportunity to increase agricultural income.

Variables	Agricultural income generation opportunities		Long-term effect of poverty alleviation
	Model 2		Model 3		Model 4
Age	−0.050*	−0.006*	−0.152***	−0.070*	−0.072*
Gender	0.070	0.066	0.004	0.009	0.031
Years of education	0.159**	0.123**	0.237***	0.185***	0.145***
Health status assignment	0.004	0.013	0.079**	0.093*	0.088
Marital status assignment	0.099	0.059	0.031	0.089	0.108
Family Size	−0.020	−0.040	0.136*	0.106*	0.119*
Ever been a poor household	0.002	−0.016	−0.032*	−0.058	−0.053*
Internet information usage behavior		0.354***			0.396***
Agricultural income generation opportunities				0.512***	0.327***
R2	0.039	0.159	0.140	0.391	0.481
Adjusting R2	0.010	0.130	0.114	0.370	0.461
F-value	1.39**	5.455***	5.396***	18.553***	23.701***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

The results of the test of mediating effects of employment opportunities for work are shown in Table 10. From the regression results in Table 10 and it can be seen that the use of Internet information use has a positive contribution to the generation of employment work opportunities for smallholder farmers and is significant at the 1% level, and hypothesis H2b holds. At the same time, the increase of employment opportunities can significantly improve the long-term effect of smallholder farmers out of poverty, and both are significantly positive when both Internet information use and employment opportunities are included, indicating the existence of the mediating role of employment opportunities between Internet information use and the long-term effect of smallholder farmers out of poverty.

Table 10.

Test of the mediating effect of job opportunities.

Variables	Employment Opportunities		Long-term effect of poverty alleviation
	Model 2		Model 3		Model 4
Age	−0.095*	−0.030*	−0.152***	−0.069*		−0.060*
Gender	0.061	0.057	0.004	0.009		0.030
Years of education	0.133*	0.091**	0.237***	0.182***		0.152***
Health status assignment	0.032	0.021	0.079**	0.093*		0.100
Marital status assignment	0.001	0.047	0.031	0.087		0.072
Family Size	−0.041*	−0.064	0.136*	0.106*		0.130*
Ever been a poor household	−0.035	−0.056	−0.032*	−0.058		−0.038*
Internet information usage behavior		0.410***				0.361***
Employment Opportunities				0.516***		0.368***
R2	0.036	0.198	0.140	0.391		0.500
Adjusting R2	0.007	0.170	0.114	0.370		0.480
F-value	1.242***	7.107***	5.396***	18.753***		25.521***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

The results of the test of entrepreneurial business opportunity as a mediating variable are shown in Table 11. According to the results shown in the table, it shows that after the inclusion of entrepreneurial business opportunity as a mediating variable, the coefficient is significantly positive and the farmer's entrepreneurial business opportunity is an effective mediating pathway and the hypothesis H2c holds.

Table 11.

Test of the mediating effect of entrepreneurial business opportunities.

Variables	Entrepreneurial business opportunities		Long-term effect of poverty alleviation
	Model 2		Model 3		Model 4
Age	−0.029*	−0.038*	−0.152***	−0.071*	−0.085*
Gender	0.002	0.006	0.004	0.088	0.007
Years of education	0.231***	0.188**	0.237***	0.185***	0.114**
Health status assignment	0.111*	0.123**	0.079**	0.093*	0.046
Marital status assignment	−0.147	−0.195*	0.031	0.089	0.015
Family Size	0.041	0.016	0.136*	0.106*	0.100**
Ever been a poor household	−0.060	−0.038	−0.032*	−0.058	−0.073
Internet information usage behavior		0.427***			0.350***
Entrepreneurial business opportunities				0.513***	0.379***
R2	0.113	0.288	0.140	0.391	0.493
Adjusting R2	0.087	0.264	0.114	0.370	0.473
F-value	4.241***	11.698***	5.396***	18.574***	24.881***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

The results of the mediated utility test for financial credit opportunities are shown in Table 12. The results show that the mediating effect of financial credit opportunities is not significant, and hypothesis H2d is not valid.

Table 12.

Intermediary effect test of financial credit opportunities.

Variables	Financial Credit Opportunities		Long-term effect of poverty alleviation
	Model 2		Model 3		Model 4
Age	−0.092*	−0.032*	−0.152***	−0.070*	−0.085*
Gender	0.228	0.224	0.004	0.009	0.095
Years of education	0.298***	0.260**	0.237***	0.185***	0.086**
Health status assignment	0.054*	0.044**	0.079**	0.093*	0.110
Marital status assignment	−0.038	−0.080*	0.031	0.089	0.058
Family Size	0.056	0.034	0.136*	0.106*	0.093**
Ever been a poor household	−0.040	−0.059	−0.032*	−0.058	−0.036
Internet information usage behavior		0.377***			0.368***
Financial Credit Opportunities				0.512***	0.383
R2	0.159	0.295	0.140	0.391	0.494
Adjusting R2	0.133	0.270	0.114	0.370	0.475
F-value	6.245***	12.074***	5.396***	18.553***	24.990***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

5.4. Effects of moderating model

5.4.1. Formal social support

The regression results of the formally supported moderating effects, following the method of the moderating effects test described above, are shown in Table 13 below. After adding formal social support, the adjusted R2 is 0.630, which is greater than the goodness of fit when only mediating variables are present, indicating that the model better explains the problem under study. Meanwhile, the regression coefficient of the cross product term of development opportunities and formal social support was 0.282, which passed the test at the 1% confidence level. This indicates that there is a positive moderating effect of formal support on the relationship between development opportunities and long-term effect of poverty alleviation among smallholder farmers, hypotheses H3 is supported.

Table 13.

Regression results of forma social supporting for moderation effects.

Variables	Long-term effect of poverty alleviation
	Model 5		Model 6	Model 7
Age	−0.152***	−0.135*	−0.058*	−0.073*
Gender	0.004	0.012	0.042	−0.046
Years of education	0.237***	0.088**	0.069**	0.080**
Health status assignment	0.079**	0.069	0.061	0.078
Marital status assignment	0.031*	0.003*	0.019*	0.016*
Family Size	0.136	0.104*	0.114	0.113
Ever been a poor household	−0.032*	−0.058	−0.003	−0.022
Development Opportunities		0.594***	0.235***	0.237***
Formal Social Support			0.599***	0.623***
Development Opportunities * Formal social support				0.282***
R2	0.140	0.481	0.568	0.645
Adjusting R2	0.114	0.461	0.551	0.630
F-value	5.396***	23.715***	33.589***	41.677***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

5.4.2. Informal social support

According to the above test of formal support, the regression results are shown in Table 14 by replacing one of the formal social support with informal social support to test the moderating role of informal support between development opportunities and the long-term effect of smallholder farmers out of poverty. As shown in the table, after adding informal social support, the adjusted R2 is 0.610, which is greater than the goodness of fit when only mediating variables are present, indicating that the model fits better after adding informal social support. The regression coefficient of the cross product term of development opportunities and informal social support was 0.302, which passed the test at the 1% confidence level, indicating that there is a positive moderating effect of informal social support on the relationship between development opportunities and long-term effect of poverty alleviation among smallholder farmers, hypotheses H4 is supported.

Table 14.

Regression results of informal social support for moderating effects.

Variables	Long-term effect of poverty alleviation
	Model 5		Model 6	Model 7
Age	−0.152***	−0.135*	−0.072*	−0.075*
Gender	0.004	0.012	0.062	−0.069
Years of education	0.237***	0.088**	0.079**	0.073**
Health status assignment	0.079**	0.069	0.065	0.050
Marital status assignment	0.031*	0.003*	0.016*	0.024*
Family Size	0.136	0.104*	0.125	0.125
Ever been a poor household	−0.032*	−0.058	−0.010	−0.015
Development Opportunities		0.594***	0.603***	0.627***
Informal social support			0.153***	0.197***
Development Opportunities * Informal social support				0.302***
R2	0.140	0.481	0.540	0.627
Adjusting R2	0.114	0.461	0.522	0.610
F-value	5.396***	23.715***	29.942***	38.454***

Note: *, ** and *** represent estimated coefficients significant at the 0.1, 0.05 and 0.01 levels, respectively.

5.5. Discussions

With the wave of the fifth industrial revolution, the extensive use of digital information technology is having a significant impact on economic growth and people's well-being. The rapid development of internet technology has provided new ideas for poverty alleviation in China's poverty-stricken areas and accelerated the pace of poverty alleviation [100], internet access can effectively reduce poverty levels [101]. The use of digital information technology improves the multidimensional poverty situation of all groups, promotes the accumulation of social capital [102], and reduces the likelihood of poverty in families owning information assets such as cable television or internet services [103,104]. It has been predicted through evolutionary game models that the effectiveness of poverty alleviation can be significantly improved by strengthening information acquisition among rural residents, and significant positive impacts on various measures for successful poverty alleviation have been produced [105]. It is generally consistent with our findings that Internet information use has a highly statistically significant positive effect on the long-term effect of poverty alleviation among smallholder farmers.

As an important way for people to access information, the use of social media can significantly increase bridging social capital [106]. The development of social networks provides families with more development opportunities and increases the chances of accessing information. Through researching the impact of regional broadband availability on local economic activity in Germany, internet penetration rate has a positive impact on employment rates, especially in rural areas where the impact is even more significant [65]. Using internet information can help residents find suitable jobs, thereby alleviating income poverty [107]. Through the “high-speed train” of information technology, poverty-stricken areas can be highly connected to external markets and resources [108,109]. Existing studies mostly focus on social capital and overlook the fundamental reason for the possible relapse into poverty of small farmers, which is the lack of development opportunities. Focused on the development opportunities, this paper explores their mediating effect between Internet information usage and poverty alleviation. Additionally, four specific aspects of development opportunities are presented. The paper finds that Internet information can help small farmers increase agricultural income, employment, entrepreneurial opportunities, and enhance the endogenous driving force of development, ultimately achieving long-term poverty alleviation effects.

Promoting close cooperation between financial institutions and government departments plays an important role in improving the efficiency of poverty alleviation work [110]. Tools, financing, and channels that improve agricultural production levels are provided to the poor (farmers) by the government, non-governmental organizations, and social enterprises, which strengthens their initiative to increase income [111,112]. Subsidies are provided by local governments to companies based on specific poverty alleviation budget plans [113], which further stimulates businesses to carry out poverty alleviation projects in impoverished areas and provides them with development opportunities, helping the impoverished population achieve sustainable poverty alleviation. This paper divides social support into two types: formal and informal social support, and clarifies the specific regulatory roles these two types of social support play in the process of converting development opportunities (Fig. 5).

Fig. 5.

Mechanism of long-term effect of the Internet information usage on poverty alleviation.

In the post-poverty alleviation era, we should gradually explore new poverty reduction concepts, policies, and methods that innovate for the economic and social development of poverty-stricken areas [114]. Relying on the advancement of digital information technology, build more convenient social networks, establish smoother information communication channels [102], and promote information symmetry among participants [105]. Actively spreading internet technology and providing internet skills training for farmers can further enhance their self-development capabilities and poverty reduction effectiveness. The focus should not only be on providing material assistance, but also on providing farmers with more opportunities for development and a level playing field to enhance their willingness and ability to participate in industrial and business activities. In terms of providing social support, multi-dimensional strategies should be adopted. Governments and non-governmental organizations can provide financial support, training, and technical support, while encouraging the establishment of mutual aid mechanisms and community-based organizational forms to provide more informal social support to farmers. This can help improve the success rate of farmers' development and sense of belonging in the community.

6. Conclusions, managerial implications and limitations

6.1. Conclusions

This study examines whether Internet information behavior can have an impact effect on the long-term effect of poverty alleviation from the perspective of poverty alleviation, then further reveals the inner mechanism of the transformation between smallholder farmers' connected information behavior and long-term effect of poverty alleviation on this basis, explores the path and boundary conditions of Internet information use on smallholder farmers' long-term effect of poverty alleviation, proposes hypotheses in combination with related studies, and verifies the mediating and moderating relationships. The findings of the study are: (1) Internet information usage has a long-term poverty alleviation effect on smallholder farmers, and the use of Internet information by smallholder farmers contributes to the achievement of their long-term effect of poverty alleviation. (2) Based on the rights-based poverty theory and starting from the perspective of opportunity, the development opportunity is explored as a realization path for Internet information use to promote the long-term effect of poverty alleviation among smallholder farmers, and the overall development opportunity is significantly present as a mediating path between Internet information use and the long-term effect of poverty alleviation among smallholder farmers. In the process of conducting the heterogeneity test it was found that agricultural income opportunities, employment opportunities in the workforce and entrepreneurial business opportunities have significant mediating effects on the relationship between Internet information behavior and the long-term effects of poverty alleviation. (3) On the basis of the mediating path, the inclusion of social support as a moderating variable improves the model fit, and both formal and informal social support play a significant positive moderating role in the process of transformation of development opportunities by smallholder farmers.

6.2. Managerial implications

Based on the study findings, we now provide the following specific recommendations: (1) Policymakers should prioritize the development of digital infrastructure in rural areas, including the expansion of internet connectivity and the establishment of information access points. Additionally, policies should be designed to promote digital literacy and ensure affordable internet services for smallholder farmers. (2) Practitioners and extension workers should actively engage with smallholder farmers to enhance their digital skills and information access. This can be achieved through targeted training programs on utilizing online platforms for agricultural best practices, market information, and financial management. Close collaboration with farmer organizations and cooperatives is crucial for effective implementation. (3) Stakeholders, including agricultural cooperatives, non-governmental organizations, and private sector entities, should collaborate to create and support online platforms that facilitate access to market information, networking opportunities, and value chain integration for smallholder farmers. These platforms should be user-friendly, localized, and tailored to the specific needs and challenges faced by smallholder farmers. (4) Financial institutions and investors should prioritize financing options for smallholder farmers that enable investments in digital technologies and facilitate access to affordable credit and insurance services. Governments can play a role in incentivizing financial institutions to develop products specifically designed for smallholder farmers.

It is important to note that these recommendations should be adapted to local contexts and stakeholders’ priorities, and further consultation with relevant actors is encouraged. We believe that implementing these recommendations will contribute to effective poverty alleviation strategies and empower smallholder farmers through enhanced information access and digital empowerment.

6.3. Limitations and future research

Despite the theoretical and practical significance of this study, there are still certain limitations that suggest possible avenues for future research. Firstly, the research subjects selected in this study mainly focused on rural residents in H Province, China. However, different countries have different situations, and each province in the same country has its own unique characteristics. In the future, the sample size can be expanded as much as possible to represent the actual situation of different regions. Secondly, in selecting indicators, this study mainly used two indicators, economic status, and sustainable income, to judge the long-term poverty alleviation of small farmers. However, the measurement of sustainable poverty alleviation is not limited to these two dimensions. In the future research can be supplemented to each multiple measurement dimensions, in order to get more accurate results, for the selection of indicators for the use of information on the Internet, it is necessary to broaden the information channels, focusing on the modern technology, such as the Internet of Things, meta-computing, and other 5G era of the application of intelligent technology in agriculture effect. In addition, the Internet information behavior used in this paper is mainly the use of Internet information, but the process of how Internet information is transformed into information effectiveness after it is acquired needs to be further explored and researched.

Ethics statement

All participants/patients (or their proxies/legal guardians) provided informed consent to participate in the study.

Author contribution statement

Wei Huang: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Shiyu Ding: Yinke Liu: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Xiaoying Song: Performed the experiments; Wrote the paper.

Shuhui Gao: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Data availability statement

The data that has been used is confidential.

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.

Appendix A. Supplementary data

The following are the Supplementary data to this article: