Does the Different Ways of Internet Utilization Promote Entrepreneurship: Evidence from Rural China
-
Xinhao Zhou
,
Nan Tang
Abstract
Entrepreneurship is widely recognized as a key driver of both innovation and economic growth. It comprises two essential components: (i) discover entrepreneurial opportunities and (ii) accumulate entrepreneurial capital. Based on the Chinese Family Panel Studies in 2018, we consider online information acquisition and online learning to be important ways of discovering entrepreneurial opportunities. Additionally, we regard online social engagement and online businesses as essential channels of entrepreneurial capital accumulation. The study reveals that entrepreneurship is positively affected by online information acquisition, online learning, online social engagement, and online businesses. The robustness of our results has been confirmed in terms of model misspecification and the reverse causality of entrepreneurship to Internet utilization. Furthermore, evidence indicates that the impact of online learning on entrepreneurship is negatively moderated by human capital. Additionally, social capital and financial capital play a role in mediating the causal pathway linking online social engagement and online businesses to entrepreneurship.
1 Introduction
Despite the positive role of entrepreneurship in driving innovation and economic growth, the level of entrepreneurship in rural areas remains significantly lower compared with that in urban areas, especially under the impact of the COVID-19 pandemic on entrepreneurship. Previous research has investigated the factors that determine entrepreneurship from various perspectives such as capital and individual characteristics (Gedajlovic et al., 2013; Nick et al., 2020; Susanne et al., 2018). Scholars have also addressed the impact of Internet utilization on entrepreneurship (Bhimani et al., 2019; Jarwar et al., 2017; Zhao, 2020). For instance, Barnett et al. (2019) found the positive impact of cell phone and Internet utilization on entrepreneurship, as well as the role of social networks and online information acquisition in mediating the impact of information and communications technology utilization on entrepreneurship. According to Tan and Li (2022), the Internet primarily facilitates entrepreneurship by making it easier for entrepreneurs to access information and secure informal financing. Moreover, the study by Liu et al. (2022) demonstrated that the determinants of necessity entrepreneurship are different from those of opportunity entrepreneurship, with the former being more likely to be impacted by Internet utilization than the latter. However, there is a lack of literature on how the different ways of Internet utilization will impact entrepreneurship, especially in terms of discovering entrepreneurial opportunities and accumulating entrepreneurial capital, which are critical components of entrepreneurship. Therefore, this research gap should be filled because the findings from China could be generalized to other similar economies as well.
Using data from the Chinese Family Panel Studies (CFPS) in 2018, our study contributes to the current literature in the following ways. First, we found that the different ways of Internet utilization can positively affect entrepreneurship. In the stage of discovering entrepreneurial opportunities, an average of 4.25 (2.96)% more people who acquire information online (or learn online) experience a transition to entrepreneurship than those who do not. In the stage of accumulating entrepreneurial capital, the likelihood of switching to entrepreneurship for those who engage in online social activities (online businesses) was, on average, 4.00 (5.79)% higher than that for those who do not. These results are robust in terms of model misspecification and the reverse causality of entrepreneurship to Internet utilization. Second, referring to the sustainable livelihood framework (Markley & Low, 2012), we explored the relationship between the different ways of Internet utilization and entrepreneurship by considering livelihood capital. Our findings reveal that the impact of online learning on entrepreneurship is negatively moderated by human capital, while social capital and financial capital play a role in mediating the causal pathway from online social engagement and online businesses to entrepreneurship.
This article is composed of six sections. Section 1 is the introduction. Section 2 presents the theoretical framework for this study and a set of hypotheses. Section 3 provides a detailed description of the data and variables adopted in the analysis. Section 4 elaborates on the empirical results regarding the relationship between Internet utilization and entrepreneurship. Section 5 involves a discussion about the findings of the study. Section 6 offers concluding remarks.
2 Theoretical Model and Hypotheses Development
In this section, we aim to review related research that serves as a foundation for this study and develop a vision of several mechanisms underlying how different ways of Internet utilization would impact entrepreneurship. Based on this review, we proposed a theoretical model and four hypotheses.
2.1 Discover Entrepreneurial Opportunities
Some studies have shown the positive impact of opportunity discovery on entrepreneurship mainly because discovering entrepreneurial opportunities is the first step in entrepreneurship. According to Shane (2000), there are two major integral elements of the identification of entrepreneurial opportunities: (i) adequate information triggering an entrepreneurial conjecture and (ii) cognitive properties that find its value.
2.1.1 Trigger an Entrepreneurial Conjecture
First of all, the process of discovering entrepreneurial opportunities requires access to adequate information, which can trigger an entrepreneurial conjecture. Therefore, those with more information are more likely to become entrepreneurs than those possessing less.
As an important ingredient of Internet applications, the search engine can be instrumental in enabling potential entrepreneurs to obtain abundant information online, thus facilitating the discovery of entrepreneurial opportunities. Tan and Li (2022) also suggested that the Internet can facilitate communication and lessen information asymmetry during people’s decision-making regarding entrepreneurship. Based on the aforementioned reviews, online information acquisition may help individuals to discover entrepreneurial opportunities and thus promote entrepreneurship. Therefore, we put forward H1.
H1: Online information acquisition has a positive impact on entrepreneurship.
2.1.2 Find Value
Second, apart from access to adequate information, the process of discovering entrepreneurial opportunities also requires access to prior knowledge of cognitive properties. For instance, Karlan and Valdivia (2011) found that entrepreneurship teaching can improve the individual’s entrepreneurial activities. Arentz et al. (2013) indicated that an entrepreneur’s prior knowledge plays a critical role in enabling him/her to exploit entrepreneurial opportunities. Therefore, if individuals possess more knowledge, they will be more likely to become entrepreneurs than those who do not.
Knowledge acquired from online applications and platforms can enrich cognitive properties, which may be essential for potential entrepreneurs. Based on the aforementioned reviews, online learning may help individuals to discover entrepreneurial opportunities and thus promote entrepreneurship. Therefore, we put forward the following hypothesis.
H2: Online learning has a positive impact on entrepreneurship.
2.2 Accumulate Entrepreneurial Capital
The ability to discover entrepreneurial opportunities is not adequate for one’s engagement in entrepreneurship. Entrepreneurial capital is also essential for the process of grasping entrepreneurial opportunities. Therefore, accumulating entrepreneurial capital is extremely crucial for potential entrepreneurs’ success.
2.2.1 Social Capital
Social capital is mostly focused on familial ties and friendships, which are connected via both formal and informal connections. Existing studies have demonstrated a positive impact of social capital on entrepreneurship. For example, Arafat et al. (2020) found that people who are socially connected to entrepreneurs are more likely to engage in entrepreneurial activities in the agricultural sector. Khoshmaram et al. (2018) indicated that social capital can directly and positively affect Iranian farmers’ entrepreneurship. Some studies have shown that the main advantage of social capital lies in its positive role in helping potential entrepreneurs acquire entrepreneurial funding through informal financing (Gedajlovic et al., 2013; Nick et al., 2020).
The Internet is profoundly changing the way people communicate. In comparison with face-to-face interaction, technological communication is more convenient, which maintains and enlarges an individual’s social capital (Wang et al., 2022; Zhao, 2020). To sum up, online social engagement may help individuals to expand social capital and thus promote entrepreneurship. Therefore, we put forward H3.
H3: Online social engagement has a positive impact on entrepreneurship.
2.2.2 Financial Capital
As a matter of fact, potential entrepreneurs are often constrained from engaging in entrepreneurship by a lack of funds (Paulson & Townsend, 2004). Due to limited collateral, people in rural China often struggle to secure adequate entrepreneurial funding from financial institutions.
Online businesses constitute a necessary component of remote transactions as they provide the infrastructure and technology needed to facilitate secure and efficient exchanges. Existing literature suggests that online businesses have a positive impact on entrepreneurship (Luo & Zeng, 2020; Vong et al., 2012). According to the findings of some studies, the main reason is that online businesses can create a huge development space for the reduction of financial transaction costs and the relief of financing constraints (Tan & Li, 2022). Based on the aforementioned reviews, online businesses may help individuals to obtain financial capital and thus promote entrepreneurship. Therefore, we proposed the following hypothesis.
H4: Online businesses have a positive impact on entrepreneurship.
Table 1 shows the four hypotheses proposed.
Hypotheses and the expected results
| Hypotheses | Expected results |
|---|---|
| H1 | Online information acquisition has a positive impact on entrepreneurship |
| H2 | Online learning has a positive impact on entrepreneurship |
| H3 | Online social engagement has a positive impact on entrepreneurship |
| H4 | Online businesses have a positive impact on entrepreneurship |
With all these discussions, the theoretical model is shown in Figure 1.
The theoretical model.
3 Data and Variables
3.1 The Data Source
CFPS refers to a nationwide household survey conducted by Peking University every 2 years. Additionally, CFPS provides a rich dataset for both academic research and policy analysis. Starting in 2010, the baseline survey of CFPS covers around 15,000 households and 30,000 individuals. We used data from the 2018 survey, which covers around 14,218 households and 37,354 individuals from 31 provinces (cities, or autonomous regions) excluding Hong Kong, Macao, and Taiwan, by matching samples and restricting the analysis to rural areas while excluding observations with missing values. The results in the final dataset contain 6,036 observations, providing a panorama of economic behavior and outcomes among rural households in China.
3.2 Variables
3.2.1 Dependent Variable
Serving as an indicator variable of an entrepreneur, the dependent variable was defined according to the engagement of family members in entrepreneurship in CFPS. If family members engaged in entrepreneurship, the value of the dependent variable is one. Otherwise, it is zero.
3.2.2 Independent Variables
In this study, we divided the independent variables into four groups:
Online information acquisition. The independent variable was defined according to the question of whether the Internet is important for information acquisition in CFPS. If the answer is yes, the value of the variable is one. Otherwise, it is zero.
Online learning. The independent variable was defined according to whether the interviewee used the Internet to study in CFPS. If the answer is yes, the value of the variable is one. Otherwise, it is zero.
Online social engagement. The independent variable was defined according to whether the interviewee used the Internet to socialize in CFPS. If the answer is yes, the value of the variable is one. Otherwise, it is zero.
Online businesses. The independent variable was defined according to whether the interviewee used the Internet to conduct commercial activities in CFPS. If the answer is yes, the value of the variable is one. Otherwise, it is zero.
3.2.3 Control Variables
Many individual and household variables that could impact entrepreneurship were also considered in this study, including variables of individual characters and variables of household characteristics, such as age, gender, health, marriage, family scale, and household wealth, as suggested by Zhang and Pan (2012).
3.2.4 Moderating Variable
The moderating variable refers to education, which is the proxy variable of human capital.
3.2.5 Mediating Variables
Social network. The mediating variable was defined according to the total household expenses of gifts for maintaining social relations in CFPS. In rural China, family relationships and friendship are the basis for social networks. Therefore, social network was chosen as the proxy variable of social capital.
Financial assets. The mediating variable was defined according to the total amount of financial assets in CFPS. Financial assets were selected as the proxy variable of financial capital.
Table 2 shows the definitions of the aforementioned variables.
Definitions of variables
| Variables | Definitions |
|---|---|
| Entrepreneurship | Family members engaged in business operation = 1, otherwise = 0 |
| Online information acquisition | Considered the Internet as an important source of access to information = 1, otherwise = 0 |
| Online learning | Used the Internet for learning = 1, otherwise = 0 |
| Online social engagement | Used the Internet to socialize = 1, otherwise = 0 |
| Online business | Used the Internet to engage in commercial activities = 1, otherwise = 0 |
| Male | Male = 1, otherwise = 0 |
| Age | Age of household head |
| Health | Self-rated health condition being relatively healthy and above = 1, otherwise = 0 |
| Married | Married = 1, otherwise = 0 |
| Family scale | The number of family members |
| Household wealth | The total amount of household wealth |
| Education | Schooling year |
| Social network | The total amount of expenses related to the maintenance of social relations |
| Financial assets | The total amount of financial assets |
Table 3 provides descriptive statistics. We can find that the percentage of entrepreneurship was around 7.6% in rural China, with the primary independent variables being the behaviors of Internet users. About 44.3% of rural households considered the Internet a critical source of information, while 14.0% used the Internet for learning purposes. Meanwhile, the proportion of households using the Internet for social activities reached 33.8%, while the proportion of households using the Internet for business was 20.7%. The final sample is composed of a total of 6,036 households.
Descriptive statistics
| Variables | Mean | Std. | Min | Max |
|---|---|---|---|---|
| Entrepreneurship | 0.076 | 0.264 | 0 | 1 |
| Online information acquisition | 0.443 | 0.497 | 0 | 1 |
| Online learning | 0.140 | 0.358 | 0 | 1 |
| Online social engagement | 0.338 | 0.473 | 0 | 1 |
| Online businesses | 0.207 | 0.405 | 0 | 1 |
| Male | 0.562 | 0.496 | 0 | 1 |
| Age | 51.675 | 14.193 | 16 | 92 |
| Health | 2.837 | 1.281 | 1 | 5 |
| Married | 0.845 | 0.362 | 0 | 1 |
| Family scale | 3.840 | 1.981 | 1 | 21 |
| Household wealth | 10.145 | 1.409 | 0 | 14.286 |
| Education | 5.983 | 4.498 | 0 | 19 |
| Social network | 7.017 | 2.530 | 0 | 11.290 |
| Financial assets | 6.950 | 4.483 | 0 | 15.009 |
4 Empirical Findings
4.1 Benchmark Regression
Table 4 shows the benchmark regression results. In the stage of discovering entrepreneurial opportunities, from column (1), the percentage of people who consider the Internet as a critical source of information was, on average, 4.25 points higher than those who do not, which was significant at the 1% level. Meanwhile, the percentage of people who use the Internet for learning was, on average, 2.96 points higher than those who do not, which was significant at the 1% level. In the stage of obtaining entrepreneurial capital, from column (2), the percentage of people who use the Internet for social activities was, on average, 4.00 points higher than those who do not, which was significant at the 1% level. The percentage of people who use the Internet for businesses was on average 5.79 points higher than those who do not, which was significant at the 1% level. Therefore, four hypotheses are validated, which supplement the conclusions of Liu et al. (2022) and Tan and Li (2022).
Benchmark regression results
| Variables | (1) | (2) | ||
|---|---|---|---|---|
| Entrepreneurial opportunity | Entrepreneurial capital | |||
| Online information acquisition | 0.642*** | |||
| (0.120) | ||||
| Online learning | 0.447*** | |||
| (0.128) | ||||
| Online social engagement | 0.604*** | |||
| (0.125) | ||||
| Online businesses | 0.881*** | |||
| (0.133) | ||||
| Male | 0.107 | 0.106 | 0.118 | 0.106 |
| (0.106) | (0.106) | (0.105) | (0.106) | |
| Age | −0.013*** | −0.018*** | −0.011** | −0.006 |
| (0.004) | (0.004) | (0.005) | (0.005) | |
| Health | 0.008 | 0.005 | 0.005 | 0.011 |
| (0.043) | (0.043) | (0.043) | (0.043) | |
| Married | 0.300* | 0.378** | 0.323* | 0.353** |
| (0.179) | (0.180) | (0.179) | (0.179) | |
| Family scale | 0.066** | 0.064** | 0.068** | 0.073*** |
| (0.027) | (0.027) | (0.027) | (0.027) | |
| Household wealth | 0.438*** | 0.451*** | 0.428*** | 0.413*** |
| (0.064) | (0.064) | (0.064) | (0.063) | |
| Regional effects | Yes | Yes | Yes | Yes |
| Pseudo R 2 | 0.087 | 0.082 | 0.085 | 0.091 |
| N | 6,036 | 6,036 | 6,036 | 6,036 |
Note: ***p < 0.01, **p < 0.05, *p < 0.1.
4.2 Endogeneity Problem
The benchmark model indicates that the different ways of Internet utilization contribute to a higher likelihood of entrepreneurship. Nevertheless, the regression results may be affected by the issue of endogeneity. Given that people use the Internet because they are already involved in entrepreneurship, the benchmark model may suffer from a reverse causality issue.
Based on CFPS data in 2016, we introduced the variables of new entrepreneurship, namely the households that engaged in entrepreneurship in 2018 but not in 2016. Table 5 shows the findings of the impact of the Internet on new entrepreneurship. From columns (1) and (2), we can see that the coefficients of independent variables are still statistically positive at the 1% level of significance.
Impact of Internet utilization on new entrepreneurship
| Variables | (1) | (2) | ||
|---|---|---|---|---|
| Entrepreneurial opportunities | Entrepreneurial capital | |||
| Online information acquisition | 0.609*** | |||
| (0.181) | ||||
| Online learning | 0.516** | |||
| (0.194) | ||||
| Online social engagement | 0.651*** | |||
| (0.190) | ||||
| Online businesses | 0.698*** | |||
| (0.203) | ||||
| Control variables | Yes | Yes | Yes | Yes |
| Regional effects | Yes | Yes | Yes | Yes |
| Pseudo R 2 | 0.074 | 0.071 | 0.075 | 0.075 |
| N | 4,792 | 4,792 | 4,792 | 4,792 |
Note: ***p < 0.01, **p < 0.05, *p < 0.1.
4.3 Robustness Check
The benchmark model indicates that the different ways of Internet utilization can raise the likelihood of entrepreneurship. However, the regression results may be biased. Therefore, referring to the method of Barnett et al. (2019), we used the propensity score matching (PSM) method to lower the risk of model misspecification.
We matched the individuals who do not acquire information (or conduct online learning, online social engagement, and online businesses) via the Internet to the comparison group by using the nearest neighbor matching algorithm (n = 4). Table 6 shows the estimated average treatment effects on treated, which are almost consistent with the results of the benchmark model. The percentage of the individuals who use the Internet for information acquisition (or online learning) was 4.5 (4.1) points higher than that of the individuals who engage in entrepreneurship. The percentage of the individuals who use the Internet for online social engagement (online businesses) was 3.9 (6.3) points higher than that of those who engage in entrepreneurship. Next, we compared the distribution of covariates in both the treatment and comparison groups before and after matching. The results of the balancing test suggest that the matching process is of high quality.
Robustness check
| Variables | (1) | (2) | ||
|---|---|---|---|---|
| Entrepreneurial opportunities | Entrepreneurial capital | |||
| Online information acquisition | 0.045*** | |||
| (0.010) | ||||
| Online learning | 0.041*** | |||
| (0.015) | ||||
| Online social engagement | 0.039*** | |||
| (0.013) | ||||
| Online businesses | 0.063*** | |||
| (0.016) | ||||
| Control variables | Yes | Yes | Yes | Yes |
| Regional effects | Yes | Yes | Yes | Yes |
| N | 5,986 | 5,986 | 5,986 | 5,986 |
Note: ***p < 0.01, **p < 0.05, *p < 0.1.
4.4 Moderating Effects
In the stage of discovering entrepreneurial opportunities, both online information acquisition and online learning positively affect entrepreneurship. Referring to the sustainable livelihood framework (Markley & Low, 2012), we explored the relationship between Internet utilization and entrepreneurship by considering human capital and found that the impacts of online information acquisition and online learning on entrepreneurship are moderated by human capital.
Table 7 shows the results of the moderating effects of human capital. From column (1), we can find that the impact of online information acquisition on entrepreneurship is not moderated by education. The coefficient of online information acquisition × education failed to pass the significance test. From column (2), we can find that the impact of online learning on entrepreneurship is negatively moderated by education. The coefficient of online learning × education was negative, suggesting that individuals with lower levels of education will be more likely to engage in entrepreneurship after using the Internet for learning.
Results of moderating effects
| Variables | (1) | (2) | ||
|---|---|---|---|---|
| Online information acquisition | 0.864*** | 0.622*** | ||
| (0.206) | (0.212) | |||
| Online learning | 1.859*** | 1.433*** | ||
| (0.287) | (0.297) | |||
| Education | 0.080*** | 0.050** | 0.116*** | 0.077*** |
| (0.020) | (0.021) | (0.015) | (0.156) | |
| Online information acquisition × Education | −0.010 | −0.010 | ||
| (0.025) | (0.026) | |||
| Online learning × Education | −0.137*** | −0.118*** | ||
| (0.030) | (0.030) | |||
| Control variables | No | Yes | No | Yes |
| Regional effects | Yes | Yes | Yes | Yes |
| Pseudo R 2 | 0.062 | 0.091 | 0.059 | 0.091 |
| N | 6,036 | 6,036 | 6,036 | 6,036 |
Note: ***p < 0.01, **p < 0.05, *p < 0.1.
4.5 Mediating Effects
In the stage of obtaining entrepreneurial capital, we consider social networks and financial assets as the proxy variables for social capital and financial capital (Liu et al., 2022). We focused on examining whether social capital and financial capital mediate the causal pathway from online social engagement and online businesses to entrepreneurship. We first explored the expansion effect of increased social networks established by online social engagement on entrepreneurship. Then we tested the mediating role of social networks, which is to examine the impact of social networks on entrepreneurship. Similarly, we first explored the expansion effect of increased financial assets accumulated through online businesses on entrepreneurship. Then we tested the mediating role of financial assets, which is to examine the impact of financial assets on entrepreneurship.
Table 8 shows the regression results of the mediating effects of entrepreneurial capital. From column (1), we can find that in comparison with the individuals who do not use the Internet to socialize, those who use the Internet to socialize seem to have stronger social networks. The coefficient of social networks remained positive and was significant at the 1% level, suggesting that stronger social networks are more likely to increase the probability of entrepreneurship, which is in line with the conclusion of Barnett et al. (2019). From column (2), we can find that in comparison with the individuals who do not use the Internet for business, those who use the Internet for business are likely to possess more financial assets. The coefficient of financial assets remained positive and was significant at the 1% level, suggesting that more financial assets are more likely to increase the probability of entrepreneurship, which is consistent with the conclusion of Luo and Zeng (2020).
Results of mediating effects
| Variables | (1) | (2) | ||
|---|---|---|---|---|
| Social network | Entrepreneurship | Financial assets | Entrepreneurship | |
| Online social engagement | 0.621*** | 0.582*** | ||
| (0.144) | (0.126) | |||
| Online businesses | 0.488*** | 0.828*** | ||
| (0.100) | (0.134) | |||
| Social network | 0.068** | |||
| (0.028) | ||||
| Financial assets | 0.057*** | |||
| (0.014) | ||||
| Control variables | Yes | Yes | Yes | Yes |
| Regional effects | Yes | Yes | Yes | Yes |
| Pseudo R 2 | 0.144 | 0.087 | 0.063 | 0.097 |
| N | 6,008 | 6,008 | 6,008 | 6,008 |
Note: ***p < 0.01, **p < 0.05, *p < 0.1.
5 Discussion
Previous studies have shown that Internet utilization can promote entrepreneurship in rural areas (Barnett et al., 2019; Tan & Li, 2022; Zhao, 2020). There are obvious limitations of research exploring the impact of different ways of Internet utilization on entrepreneurship. The findings generally lag behind theoretical and empirical development. Therefore, our study aims to describe how different ways of Internet utilization may impact entrepreneurship. Through the lenses of discovering entrepreneurial opportunities and obtaining entrepreneurial capital, we explored the impacts of different ways of Internet utilization on entrepreneurship according to the CFPS dataset in 2018. Our study is expected to make several contributions to the body of knowledge.
First, by addressing several mechanisms underlying how different ways of Internet utilization would impact entrepreneurship, the findings of this study contribute to the growing body of knowledge about entrepreneurship in the Internet age. Drawn on the views of discovering entrepreneurial opportunities and obtaining entrepreneurial capital, the hypotheses formulated from the theoretical model are supported by the findings of empirical studies. The results highlight the positive effects of online information acquisition, online learning, online social engagement, and online businesses on entrepreneurship, which may supplement the existing literature (Shane, 2000).
Second, the findings of this study contribute to the existing literature by exploring the moderating effects of human capital. In the stage of discovering entrepreneurial opportunities, we detailed the relationship between online learning and entrepreneurship by taking into account human capital. Generally, the more educated an individual with a higher level of education is, he/she is more likely to engage in opportunistic entrepreneurship (Figueroa-Armijos et al., 2012). The main reason is that education is closely related to the individual’s ability to identify opportunities. However, according to the analysis results of the moderating effects of human capital, individuals with lower education will be more likely to engage in entrepreneurship after using the Internet for learning. One possible explanation is that individuals with lower education can narrow the gap in their entrepreneurial ability through online learning, while individuals with higher education generally have been able to earn money and thus do not necessarily engage in entrepreneurship. Therefore, from this perspective, one of the main implications for the government is to provide low-education individuals, especially in rural areas, with more online learning platforms that can boost the probability of their engagement in entrepreneurship.
Another noticeable aspect is that in the stage of obtaining entrepreneurial capital, the analysis results of the mediating effects of social and financial capital indicate the role of social capital and financial capital in mediating the causal pathway from online social engagement and online businesses to entrepreneurship, respectively. This linkage has also received support from the findings of existing studies (Barnett et al., 2019; Luo & Zeng, 2020; Wang et al., 2022). One possible explanation is that in rural areas, social capital and financial capital can provide abundant entrepreneurial resources. In the Internet age, online social networks and online businesses can provide more channels for potential entrepreneurs to tackle social and financial constraints, thus promoting entrepreneurship. The validated results would provide practitioners and policy-makers with plenty of materials for thought so that they could contribute useful input to guide potential entrepreneurs as to how they can make full of the Internet to achieve the best entrepreneurial results.
6 Conclusion
While earlier studies have found the positive impact of Internet utilization on entrepreneurship, few studies have explored the influence of different ways of Internet utilization on entrepreneurship in terms of discovering entrepreneurial opportunities and accumulating entrepreneurial capital. Additionally, when considering livelihood capital, the relationship between Internet utilization and entrepreneurship has not been thoroughly discussed.
A theoretical model validated empirically using the data from CFPS in 2018 has been employed to explore this impact in terms of discovering entrepreneurial opportunities and accumulating entrepreneurial capital. The results highlight that the different ways of Internet utilization have positive effects on entrepreneurship. Specifically, in the stage of discovering entrepreneurial opportunities, the likelihood of switching to entrepreneurship was generally 4.25% higher for people who acquire information online (or learn online) than that for those who do not. In the stage of accumulating entrepreneurial capital, the likelihood of switching to entrepreneurship was generally 4.00% higher for people who engage in online social activities (online businesses) than that for those who do not. We have immunized our results from the reverse causality of entrepreneurship to the different ways of Internet utilization. Furthermore, we have used the PSM method to lower the risk of model misspecification.
When considering livelihood capital in examining the relationship between Internet utilization and entrepreneurship, we have found that the impact of online learning on entrepreneurship is negatively moderated by human capital, whereas the impact of information acquisition on entrepreneurship is not moderated by human capital. Evidence also suggests that social capital and financial capital play a role in mediating the impact of online social engagement and online businesses on entrepreneurship.
Acknowledgments
None.
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Funding information: This research was supported by “the Fundamental Research Funds for the Central Universities” from Zhongnan University of Economics and Law (2722022DS034, 202211011), National Natural Science Foundation of China (71804196).
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Author contributions: Conceptualization, X.Z.; methodology, X.Z.; software, X.Z.; validation, L.T., X.Z.; formal analysis, X.Z.; investigation, L.T.; resources, X.Z.; data curation, X.Z.; writing – original draft preparation, X.Z.; writing – review and editing, L.T., N.T.; supervision, N.T. All authors have read and agreed to the published version of the manuscript.
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Conflict of interest: The authors declare no conflict of interest.
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Data availability statement: Not applicable.
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Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
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Institutional review board statement: Not applicable.
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Supplementary materials: None.
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- Survival of the Fittest: The Long-run Productivity Analysis of the Listed Information Technology Companies in the US Stock Market
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- An Alternative Approach to Frequency of Patent Technology Codes: The Case of Renewable Energy Generation
- Environmental Taxation and International Trade in a Tax-Distorted Economy
- Foreign Investors and the Peer Effects to Payout Policies
- Segregation, Education Cost, and Group Inequality
- Does the Different Ways of Internet Utilization Promote Entrepreneurship: Evidence from Rural China
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- Is Data the New Gold? Considering Intellectual Property Protection and Regulation of Data
- Special Issue: Shapes of Performance Evaluation in Economics and Management Decision - Part I
- Path Constitution: Building Organizational Resilience for Sustainable Performance
- An Evaluation of E7 Countries’ Sustainable Energy Investments: A Decision-Making Approach with Spherical Fuzzy Sets
- Special Issue: Economic Implications of Management and Entrepreneurship - Part I
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- Does Bitcoin Affect Term Deposits? Evidence from MINT Countries
- Effects of Social Responsibility Practices on the Brand Image, Brand Awareness, and Brand Loyalty of Sponsor Businesses: A Study on Sports Clubs
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Articles in the same Issue
- Regular Articles
- Export Cutoff Productivity, Uncertainty and Duration of Waiting for Exporting
- Survival of the Fittest: The Long-run Productivity Analysis of the Listed Information Technology Companies in the US Stock Market
- A Replication of “The Effect of the Conservation Reserve Program on Rural Economies: Deriving a Statistical Verdict from a Null Finding” (American Journal of Agricultural Economics, 2019)
- An Alternative Approach to Frequency of Patent Technology Codes: The Case of Renewable Energy Generation
- Environmental Taxation and International Trade in a Tax-Distorted Economy
- Foreign Investors and the Peer Effects to Payout Policies
- Segregation, Education Cost, and Group Inequality
- Does the Different Ways of Internet Utilization Promote Entrepreneurship: Evidence from Rural China
- Reinvestigating the U.S. Consumption Function: A Nonlinear Autoregressive Distributed Lags Approach
- Regional Environment Risk Assessment Over Space and Time: A Case of China
- Unraveling Producer Price Inflation Pass-Through: Quantification, Structural Breaks, and Causal Direction
- The Relationship Between Knowledge Risk Management and Sustainable Organizational Performance: The Mediating and Moderating Role of Leadership Behavior
- Special Issue: Data Governance in the Digital Era
- From Competition Law to Platform Regulation – Regulatory Choices for the Digital Markets Act
- IP Law and Policy for the Data Economy in the EU
- Is Data the New Gold? Considering Intellectual Property Protection and Regulation of Data
- Special Issue: Shapes of Performance Evaluation in Economics and Management Decision - Part I
- Path Constitution: Building Organizational Resilience for Sustainable Performance
- An Evaluation of E7 Countries’ Sustainable Energy Investments: A Decision-Making Approach with Spherical Fuzzy Sets
- Special Issue: Economic Implications of Management and Entrepreneurship - Part I
- Organizational Integration, Knowledge Management, and Sustainable Entrepreneurship for SMEs in Developing Economies
- Does Bitcoin Affect Term Deposits? Evidence from MINT Countries
- Effects of Social Responsibility Practices on the Brand Image, Brand Awareness, and Brand Loyalty of Sponsor Businesses: A Study on Sports Clubs
- The Effect of Market and Technological Turbulence on Innovation Performance in Nascent Enterprises: The Moderating Role of Entrepreneur’s Courage
