Next Article in Journal
Development and Application of an Intelligent Assessment System for Mathematics Learning Strategy among High School Students—Take Jianzha County as an Example
Previous Article in Journal
Development of High-Performance Fiber Cement: A Case Study in the Integration of Circular Economy in Product Design
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 19
10.3390/su141912266
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Role of Internet on Agricultural Sector Performance in Global World

by
Arif Imam Suroso
1,*,
Idqan Fahmi
1 and
Hansen Tandra
2
1
School of Business, IPB University, Bogor 16151, Indonesia
2
Department of Resource and Environmental Economics, IPB University, Bogor 16151, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12266; https://doi.org/10.3390/su141912266
Submission received: 24 August 2022 / Revised: 20 September 2022 / Accepted: 22 September 2022 / Published: 27 September 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
The Internet is considered to be an important factor in accelerating economic growth. There is a statistic regarding the inline of higher economic growth with higher Internet access. However, the impact of the Internet on agriculture as a real sector regarding its contribution on economic growth has not been explored in detail. The purpose of this study is to analyze the global effect of the Internet on agricultural sector performance. Static panel regression was used in this study by involving 126 countries using data from World Bank Open Data (2012–2019). Extended analysis was employed by applying the regression into two classifications, namely, countries by continent and the country composition of World Economic Outlook (WEO) groups. The result of this study shows that there exists a positive and significant global effect from Internet users, fixed broadband subscriptions, and secure Internet servers on agricultural sector performance. Additionally, the positive effect of Internet variables on agriculture was only found in Africa, Asia, and Oceania. In the country composition of WEO groups, there existed a positive and significant effect of Internet users and fixed broadband subscriptions on agricultural sector performance in economies classified as emerging and developing. This implies that the role of the Internet on agriculture is relatively higher in developing countries. Therefore, policymakers in Africa, Asia, Oceania, and emerging and developing economies (WEO Groups) must consider the role of Internet to improve agricultural sectors.

1. Introduction

The Internet has been seen as an accelerator of economic growth for several decades. Previous studies found that Internet technology impacts economic growth positively [1,2,3] or negatively [4]. The development of the Internet is one of the more significant outcomes of information technology and communication (ICT). The Internet also has an effect on the global economy by changing industrial activities from the individual-country level to the global level [5]. It may be utilized to boost economic output from a real sector, particularly in agricultural production [6,7,8]. The Internet could be used to develop the agricultural industry [9]. According to World Bank Open Data, in 2022, countries with the most Internet access in terms of user numbers, infrastructure, and security are better developed [10].
Figure 1 shows real-sector performance growth proxied by added value. There was more stable performance from the agricultural sector than that from the manufacturing and service industries from 2001 until 2020. However, a strange difference in all real sectors was found between 2009 and 2010 due to the global financial crisis in 2008 compounding the performance of real sectors [11]. This suggests that there are factors that might impact an agricultural sector’s stable performance. The Internet might be one of the main factors for enhancing and maintaining agricultural performance [12]. We also found that there are various applications of Internet technologies on agriculture, including Internet of Things (IoT) applications on agricultural productivity [13,14]. Empirical applications can be used for tracing agricultural food products [15,16]. Moreover, a decision support system involving the Internet also improves the agricultural sector [17,18,19]. However, earlier studies focused solely on practical Internet usage. Specific studies on the role of the the Internet on global agricultural sector performance covering all countries are uncommon.
Agriculture is an important global sector, whether a country is developed, developing, or underdeveloped [20]. This sector supports the national economy by supplying food for people and employment for the labor force. Agriculture is the sole supplier of energy, and a fundamental input to all other economic fields and the economic market for finished commodities, emphasizing its dependability [21]. When compared to other economic sectors, the agricultural sector contributes significantly to the GDP of agricultural economies and employs more than half of their overall workforce [22]. Therefore, policymakers must consider this sector in order to maintain the stable economic performance of the country.
The influence of technological progress on agriculture was confirmed by analyzing multiple studies. Lio dan Liu [23] revealed that the ICT index was positive on agricultural productivity in 81 countries in 1995–2000, derived from Internet users per 100 people, the number of personal computers per 100 people, cellular phones per 100 people, and telephone mainlines per 100 people (teledensity). This study also revealed that the use of updated industrial inputs in agricultural output is dependent on an information and communication infrastructure. However, the empirical findings of this study suggest that thenew ICT may be a factor in the discrepancy in overall agricultural productivity between countries.
Hopestones [24] found that ICT development has an effect on agricultural production in Africa, involving 34 African countries from 2000 to 2011. Subsequently, tertiary education implicated the variable proxied by telephone lines and the Internet. Ali et al. [25] reported a positive and significant effect of ICT from television toward agricultural productivity in Zambia. Evans [26] revealed the effect of mobile phones and the Internet on agricultural value-added in 44 African countries from 2001 until 2015. There was a positive and significant effect from mobile penetration. However, the effect of Internet usage was negative.
Oyelami et al. [27] investigated the effect of an ICT infrastructure on agricultural sector performance in Sub-Saharan Africa using a panel autoregressive distributed lag (ARDL) approach over the course of 23 years (1995–2017) in 39 SSA countries. This study found that the impact of an ICT infrastructure has a positive effect on agricultural sector performance in the long run. However, there was no evidence to support this claim in the short run. As a result, the study suggested that increased investment in an ICT infrastructure be approached with caution. On the basis of previous studies, we did not find a different effect of the Internet by comparing the three indicators of the Internet on agricultural sector performance. Moreover, detailed findings regarding the three indicators of the Internet on agriculture based on several continents were not identified in previous research. We used three aspects of Internet variables, namely, users, infrastructure, and securities. In summary, the three main questions of this research are as follows:
Q1: Which Internet variable has the most influence on the agricultural sector (user, infrastructure, or securities)?
Q2: What is the effect of the Internet on agricultural sector performance by classifying the region of the country?
Q3: What is the effect of the Internet on agricultural sector performance by classifying the economy of the country?
Therefore, this research examines the global role of the Internet on agricultural sector performance with two classifications (the region and economy of the country).

2. Methodology and Data

According to the aims of this study, we used the grand theory of endogenous growth from Solow and Romer. This theory came from the Cobb–Douglas production function as the foremost common production form for estimating agricultural production [23]. Furthermore, this theory was developed by including the input function, namely, capital and labor [28], adding the technological progress variable [29]. There are several recent studies that used this concept to reveal the impact of technology [23,30,31]. The model specification utilized in this study was based on Romer’s model [29], which was designed in response to the shortcomings of the Solow growth model. The Solow growth model’s production function suggests that the output from a country, sector, or industry is a function of capital and labor, as indicated below:
Y = F(KL)
The Romer framework added technology as an endogenous variable that may be proxied as the Internet from users, infrastructures, and securities. According to Romer, there is a consideration of the investment in research technologies being an endogenous component in the acquisition of new knowledge by rational profit maximization enterprises. Internet became the technology with higher prospect to utilizing the country output through digitalization of production components. Romer endogenous growth theory says that the actual output of sectors is a function of technology, capital, and labor inputs, and defines the aggregate production function as follows:
Y = F A(KL)
where Y is the output, F is the function of the input, K is the capital, and L is the labor. K and L were used for production, and A is the technological input proxied by Internet users, infrastructure, and securities on the basis of several previous studies. In this study, we employed this grand theory by using several indicators that can be representd as the output and input factors. The equation for this study can be written as follows:
Log(Agri)it = β0 + β1Log(GFCF)it + β2Log(EMP)it + β3INTit + β4Log(FBS)it + β5Log(SIS)it + εit
where Agri is the agricultural sector’s value-added in country i and period t, GFCF is the agricultural gross fixed capital formation in country i and period t as the capital input, EMP is agricultural employment in country i and period t as the labor input, INT is Internet users in country i and period t, FBS is the fixed broadband subscription in country i and period t, SIS is secure Internet servers in country i and period t, ε is the error term, and log is the natural logarithm. The last three variables (INT, FBS, and SIS) are represented as the technological Internet input in this study.
Panel regression was applied through three estimations, namely, ordinary least squares (OLS), the random-effect model (REM), and the fixed-effect model (FEM). This approach is relevant in examining the global impact of Internet variables on agricultural sector performance, classified into countries by continent and the country composition of WEO groups. However, the correlation matrix was initially produced to check for multicollinearity, and the regression value had to be less than 0.8 or 0.9 [32,33]. After estimating the three regression models, we subsequently used the Chow (FEM or OLS) and Hausmann (FEM or REM) tests to choose the best model for explaining the determinants.
The dataset of this study were compiled from the FAO and World Bank Open Data websites. Analysis was based on yearly data from a cross-section of 126 countries from 2012 until 2019. Specific descriptions of the variables, sources, and expected signs are summarized in Table 1. Additionally, we extended our research by identifying the effect of the Internet on agricultural sector performance with two classifications: list of countries by continent and the country composition of World Economic Outlook (WEO) groups. These classifications are in Table A1 (countries by continent) and Table A2 (country composition of WEO groups).

3. Results

3.1. Trends of Internet Variables

Figure 2 shows the global growth of Internet variables according to users, infrastructures, and securities. There was a positive global trend in Internet development from 2011 until 2020. Secure Internet servers had better growth performance than that of Internet users and fixed broadband subscriptions. On the other hand, fixed broadband subscriptions and Internet users were relatively stable. On this basis, we concluded that the level of Internet development grows with several fluctuations in certain periods. The concern of this study is the extent to which this upward trend could affect the performance of global agricultural output.
Figure 3 below reveals that agriculture is globally the sector that has the lowest added value compared to the service and manufacturing sectors. However, this sector was relatively stable, with positive growth from 2001 to 2020. In particular, a factor could maintain the agricultural sector due to stable performance. This sector must be improved by adopting a technology as important input production on the basis of several previous studies [34,35]. The growth of the Internet is in line with the stable performance of the global agricultural sector.

3.2. The Effect of the Internet on Agricultural Sector Performance (Global)

The descriptive statistics shown in Table 2 imply that the data were relatively distributed by investigating the mean and standard deviation values (std. dev. lower than the mean value). Other information is the median, maximum, minimum, and N (sample). Next, we ran the matrix correlation as shown in Table 3. This table shows that there were no multicollinearity issues due to values below than 0.9. Therefore, the estimation of panel regression could be applied (Table 4).
In the panel regression, we found that the suitable model to be established was FEM on the basis of the Hausman test. The result shows that the Solow growth variables, proxied by gross fixed capital formation (GFCF) and agricultural employment (EMP), were positive and significant on agricultural value-added. There was a positive effect from the Internet, consisting of Internet users (INT), fixed broadband subscription (FBS), and secure Internet servers (SIS). The R-squared in FEM reached 99.8%, measuring the degree of the effect from variables in the model. The rest of this value (0.2%) was the other variable excluded in the model.

3.3. The Effect of the Internet on Agricultural Sector Performance (Two Classifications)

In advanced analysis, we classified all of our observations (126 countries) into two groups, namely, countries by continent and country composition by WEO group, presented in Table 5 and Table 6, respectively. We found that the estimation of the Internet effect on agricultural sector performance was based on a fixed-effect model due to a Hausmann test value below 5%. Table 5 shows the impact of Internet users on agricultural sector performance in Asia, Oceania, and Africa. However, this result was not found in the European and American regions. Fixed broadband subscriptions were only found in Asia and Oceania. These regions (Asia, Oceania, and Africa) dominantly comprise developing countries and depend heavily on the agricultural sector [36]. Furthermore, significant GFCF and EMP effects were only simultaneously found in the African continent. The other regression also found a similar result, with a significant positive impact from Internet users and secure Internet servers on agricultural sector performance in emerging and developing economies.

4. Discussion

This study reveals that there was a positive effect of Internet variables users, fixed broadband subscriptions, and secure Internet servers on agricultural sector performance, proxied by value added. The result shows that all Internet variables are essential factors for global agricultural sector performance. The adoption of the Internet in microscopes could increase the productivity of agricultural sectors, implicating on the aggregate condition of the economy proxied by agricultural real value added [37]. ICT development must be considered to be an essential factor for maintaining the sustainability of the agricultural sector because the lower adoption of modern agricultural technologies implicates a smaller agricultural output [38]. The higher effect was derived from FBS, meaning that the change in FBS improved agricultural sector performance to 1.4% (Table 4). This means that the Internet infrastructure is the first consideration to generate the effect of the Internet on agriculture. The development of infrastructure could lead to more users following the ease and usefulness of accessible Internet [39]. An improvement in Internet infrastructure also implicates the need for secure Internet development access, especially in public places [40]. Therefore, all countries must improve the quality of infrastructure rather than promote the usage of the Internet and the development of secure Internet. Internet technology has recently been seen as the global accelerator of economic growth and international trade for several countries [3].
The additional regression with two classifications revealed that Internet variables are important for agricultural sector performance in emerging and developing economies in African, Asian, and Oceanian countries. This is supported by a previous result that the impact of the Internet is relatively found in developing economies such as African countries [24,27]. Internet users are an important variable due to these significant effects from these regressions. However, Internet infrastructures are an important variable for emerging and developing economies to engage with the agricultural sector, meaning that an increase in FBS increases agricultural sector performance to 1.3% (Table 6). This was higher than that of Internet users, with a smaller value of 0.3%. Therefore, policy related to the improvement of Internet infrastructures and users must be considered. Furthermore, these results also align with the grand growth theory of Solow and Romer, implying the importance of technology variables on economic growth. On the basis of our empirical findings, the Internet could be an essential factor to enhance the value added of agriculture.

5. Conclusions

This study extended the knowledge of Internet variables on agricultural value added, represented as the output of real sectors on the basis of three aspects: Internet (1) infrastructure, (2) users, and (3) security. The results show that all Internet variables had a positive effect on global agricultural sector performance. In addition, we found an effect of two Internet variables, namely, FBS and INT, on agriculture based on continent and the country composition by WEO group. In the future, technological development could be an important factor for increasing the output of the agricultural sector. However, other input factors such as land and employment should still considered. As a result, proper assistance efforts from governments are required to utilize Internet technology, particularly in developing economies, in order to influence agricultural sector performance in the long term.
We also conclude that Internet variables are relatively important for developing countries, consisting of African, Asian, and Oceanian countries or emerging and developing economies. Advanced economies, dominantly in European and several American countries, had relatively higher values in all Internet indicators than those of emerging and developing economies. Therefore, the impact of Internet indicators in advanced-economy countries was relatively not affected in our estimations in Table 6. The evaluation of Internet variables on real-sector performance only considered the agricultural sector due to the availability of data, especially regarding Solow and Romer growth variables (GFCF and EMP). This is the main limitation in our research. Future studies can explore the effect of Internet variables on global service or manufacturing sectors.

Author Contributions

A.I.S., I.F. and H.T. have contributed equally to this study. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Education and Culture, Directorate General of Higher Education of the Republic of Indonesia, funding this research with the scheme of Penelitian Dasar Kompetitif Nasional (PDKN) (derived contract number: 09/E5/PG.02.00PT/2022) with the title “Dampak Internet terhadap Sektor Riil dan Perdagangan Global”.

Data Availability Statement

Not Applicable.

Acknowledgments

The authors would like to acknowledge the Ministry of Education and Culture, Republic of Indonesia for supporting and giving the opportunity to perform this research.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Countries Classified by Continent.
Table A1. Countries Classified by Continent.
ContinentsCountries
AfricaAlgeria, Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Chad, Côte d’Ivoire, Egypt, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Malawi, Mauritania, Mauritius, Morocco, Mozambique, Namibia, Nigeria, Rwanda, Senegal, South Africa, Togo, Tunisia, United Republic of Tanzania, Zambia, and Zimbabwe.
Asia and OceaniaAlbania, Armenia, Australia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, China, Cyprus, Georgia, India, Indonesia, Iran (Islamic Republic of), Israel, Japan, Kazakhstan, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Malaysia, Maldives, Mongolia, Nepal, New Zealand, Oman, Pakistan, Philippines, Republic of Korea, Singapore, Thailand, Timor-Leste, Türkiye, United Arab Emirates and Uzbekistan.
AmericaArgentina, Bahamas, Belize, Bolivia (Plurinational State of), Brazil, Canada, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Lucia, Suriname, United States of America, and Uruguay.
EuropeAustria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland.
Source: Statistics Times (2019) [41].
Table
Table A2. Countries Classified by WEO Group.
Table A2. Countries Classified by WEO Group.
ClassificationsCountries
Advanced economiesAustralia, Austria, Belgium, Canada, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, New Zealand, Norway, Portugal, Republic of Korea, Singapore, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom of Great Britain and Northern Ireland, and United States of America.
Emerging and developing economiesAlbania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bahamas, Bahrain, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Bosnia and Herzegovina, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cameroon, Chad, Chile, China mainland, Colombia, Costa Rica, Côte d’Ivoire, Croatia, Dominican Republic, Ecuador Egypt, El Salvador, Gabon, Gambia, Georgia, Ghana, Guinea, Honduras, Hungary, India, Indonesia, Iran (Islamic Republic of), Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lesotho, Malawi, Malaysia, Maldives, Mauritania, Mauritius, Mexico, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Nigeria, North Macedonia, Oman, Pakistan, Panama, Paraguay, Peru, Philippines, Poland, Romania, Russian Federation, Rwanda, Saint Lucia, Senegal, Serbia, South Africa, Suriname, Thailand, Timor-Leste, Togo, Tunisia, Türkiye, Ukraine, United Arab Emirates, United Republic of Tanzania, Uruguay, Uzbekistan, Zambia, and Zimbabwe.
Source: IMF (2022) [42].

References

  1. Choi, C.; Hoon Yi, M. The effect of the Internet on economic growth: Evidence from cross-country panel data. Econ. Lett. 2009, 105, 39–41. [Google Scholar] [CrossRef]
  2. Manyika, J.; Roxburgh, C. The great transformer: The impact of the Internet on Economic Growth and Prosperity; McKinsey Global Institute: Washington, DC, USA, 2011. [Google Scholar]
  3. Meijers, H. Does the internet generate economic growth, international trade, or both? Int. Econ. Econ. Policy 2014, 11, 137–163. [Google Scholar] [CrossRef]
  4. Maurseth, P.B. The effect of the Internet on economic growth: Counter-evidence from cross-country panel data. Econ. Lett. 2018, 172, 74–77. [Google Scholar] [CrossRef]
  5. Buevich, A.; Karamova, O.; Sumarokov, E. Improvement of the institutional structure of the real sector under the conditions of the digital economy. In Studies in Computational Intelligence; Springer: Cham, Switzerland, 2019; Volume 826. [Google Scholar]
  6. Kaloxylos, A.; Wolfert, J.; Verwaart, T.; Terol, C.M.; Brewster, C.; Robbemond, R.; Sundmaker, H. The Use of Future Internet Technologies in the Agriculture and Food Sectors: Integrating the Supply Chain. Procedia Technol. 2013, 8, 51–60. [Google Scholar] [CrossRef]
  7. Khan, F.A.; Abubakar, A.; Mahmoud, M.; Al-Khasawneh, M.A.; Alarood, A.A. Cotton crop cultivation oriented semantic framework based on IoT smart farming application. Int. J. Eng. Adv. Technol. 2019, 8, 480–484. [Google Scholar]
  8. Kour, V.P.; Arora, S. Recent Developments of the Internet of Things in Agriculture: A Survey. IEEE Access 2020, 8, 129924–129957. [Google Scholar] [CrossRef]
  9. Heang, J.F.; Khan, H.U. The Role of Internet Marketing in the Development of Agricultural Industry: A Case Study of China. J. Internet Commer. 2015, 14, 65–113. [Google Scholar] [CrossRef]
  10. World Bank. World Development Indicator (WDI) [Internet]. 2022. Available online: https://data.worldbank.org/ (accessed on 1 August 2022).
  11. Stiglitz, J.E. Lessons from the Financial Crisis and Their Implications for Global Economic Policy. Acad. Commons 2018, 227–238. [Google Scholar] [CrossRef]
  12. May, J.; Karugia, J.; Ndokweni, M. Information and Communication Technologies and Agricultural Development in sub-Saharan Africa. 2007. Available online: https://www.africaportal.org/publications/information-and-communication-technologies-and-agricultural-development-in-sub-saharan-africa-transformation-and-employment-generation/ (accessed on 3 August 2022).
  13. Rehman, A.; Saba, T.; Kashif, M.; Fati, S.M.; Bahaj, S.A.; Chaudhry, H. A Revisit of Internet of Things Technologies for Monitoring and Control Strategies in Smart Agriculture. Agronomy 2022, 12, 127. [Google Scholar] [CrossRef]
  14. Srivastava, A.; Dashora, K. Application of blockchain technology for agrifood supply chain management: A systematic literature review on benefits and challenges. Benchmarking 2022. [Google Scholar] [CrossRef]
  15. Feng, H.; Wang, X.; Duan, Y.; Zhang, J.; Zhang, X. Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges. J. Clean. Prod. 2020, 260, 121031. [Google Scholar] [CrossRef]
  16. Suroso, A.I.; Rifai, B.; Hasanah, N. Traceability System in Hydroponic Vegetables Supply Chain Using Blockchain Technology. Int. J. Inf. Manag. Sci. 2021, 32, 347–361. [Google Scholar] [CrossRef]
  17. Suroso, A.I.; Ramadhan, A. Decision support system for agribusiness investment as e-Government service using computable general equilibrium model. In Proceedings of the Advances in Intelligent and Soft Computing; Springer: Berlin/Heidelberg, Germany, 2012; Volume 144 AISC. [Google Scholar]
  18. Suroso, A.I.; Ramadhan, A. Structural path analysis of the influences from smallholder oil palm plantation toward household income: One aspect of e-Government initative. Adv. Sci. Lett. 2014, 20, 352–356. [Google Scholar] [CrossRef]
  19. Panetto, H.; Lezoche, M.; Hernandez Hormazabal, J.E.; del Mar Eva Alemany Diaz, M.; Kacprzyk, J. Special issue on Agri-Food 4.0 and digitalization in agriculture supply chains—New directions, challenges and applications. Comput. Ind. 2020, 116, 103188. [Google Scholar] [CrossRef]
  20. Yadav, P.; Sharma, A.K. Agriculture Credit in Developing Economies: A Review of Relevant Literature. Int. J. Econ. Financ. 2015, 7, 219–244. [Google Scholar] [CrossRef]
  21. Chatterjee, R. Indian agriculture and role of agricultural extension system to cope up with COVID-19 crisis. Food Sci. Rep. 2020, 1, 10–15. [Google Scholar]
  22. Mondiale, B. World Development Report, 2008: Agriculture for Development; World Bank: Washington, DC, USA, 2008; Volume 45. [Google Scholar]
  23. Lio, M.; Liu, M.C. ICT and agricultural productivity: Evidence from cross-country data. Agric. Econ. 2006, 34, 221–228. [Google Scholar] [CrossRef]
  24. Hopestone, K.C. The role of ICTs in agricultural production in Africa. J. Dev. Agric. Econ. 2014, 6, 279–289. [Google Scholar] [CrossRef]
  25. Ali, S.; Jabeen, U.A.; Nikhitha, M. Impacts of ICTs on agricultural productivity. Eur. J. Bus. Econ. Account. 2016, 4, 82–92. [Google Scholar]
  26. Evans, O. Digital Agriculture: Mobile Phones, Internet & Agricultural Development in Africa; University Library of Munich: Munich, Germany, 2018; Volume 7–8. [Google Scholar]
  27. Oyelami, L.O.; Sofoluwe, N.A.; Ajeigbe, O.M. ICT and agricultural sector performance: Empirical evidence from sub—Saharan Africa. Future Bus. J. 2022, 8, 1–13. [Google Scholar] [CrossRef]
  28. Solow, R.M. A contribution to the theory of economic growth. Q. J. Econ. 1956, 70, 65–94. [Google Scholar] [CrossRef]
  29. Romer, P.M. Increasing Returns and Long-Run Growth. J. Polit. Econ. 1986, 94, 1002–1037. [Google Scholar] [CrossRef]
  30. Chitedze, I.; Nwedeh, C.C.N.; Adeola, A.; Abonyi, D.C.C. An econometric analysis of electricity consumption and real sector performance in Nigeria. Int. J. Energy Sect. Manag. 2021, 15, 855–873. [Google Scholar] [CrossRef]
  31. Asongu, S.A.; Odhiambo, N.M. Foreign direct investment, information technology and economic growth dynamics in Sub-Saharan Africa. Telecomm. Policy 2020, 44, 101838. [Google Scholar] [CrossRef]
  32. Franke, G.R. Multicollinearity. In Wiley International Encyclopedia of Marketing; Wiley-Blackwell: Hoboken, NJ, USA, 2010. [Google Scholar]
  33. Senaviratna, N.A.M.R.; Cooray, T.M.J.A. Diagnosing Multicollinearity of Logistic Regression Model. Asian J. Probab. Stat. 2019, 5, 1–9. [Google Scholar] [CrossRef]
  34. Chege, S.M.; Wang, D. The influence of technology innovation on SME performance through environmental sustainability practices in Kenya. Technol. Soc. 2020, 60, 101210. [Google Scholar] [CrossRef]
  35. Ayim, C.; Kassahun, A.; Addison, C.; Tekinerdogan, B. Adoption of ICT innovations in the agriculture sector in Africa: A review of the literature. Agric. Food Secur. 2022, 11, 1–16. [Google Scholar] [CrossRef]
  36. Mendelsohn, R.; Dinar, A. Climate change, agriculture, and developing countries: Does adaptation matter? World Bank Res. Obs. 1999, 14, 277–293. [Google Scholar] [CrossRef]
  37. Madushanki, A.A.R.; Halgamuge, M.N.; Wirasagoda, W.A.H.S.; Syed, A. Adoption of the Internet of Things (IoT) in agriculture and smart farming towards urban greening: A review. Int. J. Adv. Comput. Sci. Appl. 2019, 10. [Google Scholar] [CrossRef]
  38. Gupta, A.; Ponticelli, J.; Tesei, A. Information, Technology Adoption and Productivity: The Role of Mobile Phones in Agriculture. SSRN Electron. J. 2021. [Google Scholar] [CrossRef]
  39. Shin, D.H. User acceptance of mobile Internet: Implication for convergence technologies. Interact. Comput. 2007, 19, 472–483. [Google Scholar] [CrossRef]
  40. Chatzimichail, A.; Chatzigeorgiou, C.; Tsanousa, A.; Ntioudis, D.; Meditskos, G.; Andritsopoulos, F.; Karaberi, C.; Kasnesis, P.; Kogias, D.G.; Gorgogetas, G.; et al. Internet of things infrastructure for security and safety in public places. Information 2019, 10, 333. [Google Scholar] [CrossRef] [Green Version]
  41. Statistic Times. List of Countries by Continents. 2019. Available online: https://statisticstimes.com/geography/countries-by-continents.php (accessed on 5 August 2022).
  42. International Monetary Fund [IMF]. World Economic Outlook, Database—WEO Groups and Aggregates Information. 2022. Available online: https://www.imf.org/external/pubs/ft/weo/2021/02/weodata/groups.htm (accessed on 3 March 2022).
Sustainability 14 12266 g001 550
Figure 1. Global real-sector value-added growth in percentages (%). Source: World Bank Open Data (2022).
Figure 1. Global real-sector value-added growth in percentages (%). Source: World Bank Open Data (2022).
Sustainability 14 12266 g001
Sustainability 14 12266 g002 550
Figure 2. Growth of Internet variables of users, infrastructures, and securities in percentages (%). Source: World Bank (2022).
Figure 2. Growth of Internet variables of users, infrastructures, and securities in percentages (%). Source: World Bank (2022).
Sustainability 14 12266 g002
Sustainability 14 12266 g003 550
Figure 3. Global real-sector value added in million USD, with 2015 USD as the constant. Source: World Bank (2022).
Figure 3. Global real-sector value added in million USD, with 2015 USD as the constant. Source: World Bank (2022).
Sustainability 14 12266 g003
Table
Table 1. Operational variables.
Table 1. Operational variables.
VariablesUnit SourceExpected Sign
Log(Agri)Constant, 2015 USD World Bank Open Data−
Log(GFCF)Value Prices, 2015 USDFAOStat+
Log(EMP)1000 PersonsFAOStat+
INT% Internet usersWorld Bank Open Data+
Log(FBS)SubscriptionsWorld Bank Open Data+
Log(SIS)Distinct numbersWorld Bank Open Data+
Source: Authors (2022).
Table
Table 2. Descriptive statistics (all countries).
Table 2. Descriptive statistics (all countries).
VariablesMeanMedianMaximumMinimumStd. Dev.N
LOG(AGRI)22.03721.99027.69217.3691.8971008
LOG(GFCF)6.2916.37311.9250.8162.1061008
LOG(EMP)6.2336.22112.4550.0452.3231008
INT54.44258.13999.7011.22027.8811008
LOG(FBS)13.20113.55719.9233.7142.6101008
LOG(SIS)7.7107.55117.5220.0003.2451008
Table
Table 3. Matrix correlation.
Table 3. Matrix correlation.
VariablesLog(GFCF)Log(EMP)INTLog(FBS)Log(SIS)
Log(GFCF)1.000
Log(EMP)0.5711.000
INT0.211−0.5441.000
Log(FBS)0.7820.2140.5691.000
Log(SIS)0.6730.0000.7170.8251.000
Table
Table 4. Panel regression (all countries).
Table 4. Panel regression (all countries).
VariablesOLSFEMREM
C15.275 ***20.546 ***17.533 ***
LOG(GFCF)0.584 ***0.135 ***0.326 ***
LOG(EMP)0.310 ***0.034 *0.287 ***
INT0.003 **0.003 ***0.000
LOG(FBS)0.100 ***0.014 **0.038 ***
LOG(SIS)−0.040 ***0.011 ***0.020 ***
R-squared0.9520.9980.599
F-statistic3942.4654472.101***299.879 ***
N100810081008
Chow test-218.302***-
Hausman test--653.154 ***
Note: *, **, and *** = significant at 10%, 5%, and 1%, respectively.
Table
Table 5. Panel regression (by continent).
Table 5. Panel regression (by continent).
VariablesAsia and OceaniaAfricaEuropeAmerica
FEMREMFEMREMFEMREMFEMREM
C21.173 ***17.311 ***17.679 ***16.160 ***18.69012.040 ***20.051 ***16.790 ***
LOG(GFCF)0.067 ***0.269 ***0.196 ***0.291 ***0.0190.080 ***0.266 ***0.561 ***
LOG(EMP)−0.0040.346 ***0.384 ***0.514 ***0.0450.175 ***−0.0040.104 ***
INT0.003 ***−0.0010.005 ***0.004 ***0.001−0.003 *0.002−0.002
LOG(FBS)0.042 ***0.067 ***−0.0020.0050.1900.599 ***0.0200.087 **
LOG(SIS)0.0090.024 ***0.0070.0020.007−0.0040.0150.029 **
R-squared0.9990.6870.9980.7010.9970.6130.9990.730
F-statistic7971.846 ***120.223 ***3272.359 ***117.339 ***2404.758 ***91.861 ***4281.781 ***92.021 ***
N280280256256296296176176
Chow test227.732 *** 184.364 *** 155.964 *** 123.846 ***
Hausman test 275.360 *** 99.298 *** 59.857 *** 91.395 ***
Notes: *, **, and *** = significant at 10%, 5%, and 1%, respectively.
Table
Table 6. Panel regression (by country composition of WEO groups).
Table 6. Panel regression (by country composition of WEO groups).
VariablesAdvanced EconomiesEmerging and Developing Economies
FEMREMFEMREM
C21.213 ***12.246 ***20.373 ***17.564 ***
LOG(GFCF)0.0050.120 ***0.173 ***0.351 ***
LOG(EMP)−0.0300.215 ***0.036 *0.272 ***
INT0.001−0.004 **0.003 ***0.000
LOG(FBS)0.0650.564 ***0.0100.026 ***
LOG(SIS)0.010−0.0010.013 ***0.021 ***
R-Square0.9980.5470.9990.648
F-Statistics3440.032 ***64.148 ***4904.065269.184
N272272736736
Chow Test221.099 *** 208.306 ***
Hausman Test 89.789 *** 499.726 ***
Note: *, **, and *** = significant at 10%, 5%, and 1%, respectively.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Suroso, A.I.; Fahmi, I.; Tandra, H. The Role of Internet on Agricultural Sector Performance in Global World. Sustainability 2022, 14, 12266. https://doi.org/10.3390/su141912266

AMA Style

Suroso AI, Fahmi I, Tandra H. The Role of Internet on Agricultural Sector Performance in Global World. Sustainability. 2022; 14(19):12266. https://doi.org/10.3390/su141912266

Chicago/Turabian Style

Suroso, Arif Imam, Idqan Fahmi, and Hansen Tandra. 2022. "The Role of Internet on Agricultural Sector Performance in Global World" Sustainability 14, no. 19: 12266. https://doi.org/10.3390/su141912266

APA Style

Suroso, A. I., Fahmi, I., & Tandra, H. (2022). The Role of Internet on Agricultural Sector Performance in Global World. Sustainability, 14(19), 12266. https://doi.org/10.3390/su141912266

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop