Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Processing
2.3. Methods
2.3.1. Main Network Features
- ①
- Network density:
- ②
- Clustering coefficient
- ③
- Average path length
- ④
- Intermediary centrality
2.3.2. Quadratic Assignment Problem (QAP) Analysis
3. Results
3.1. Analysis of the Evolution Modes of Chili Pepper Industry Cluster
3.1.1. Analysis of Social Network Characteristics
3.1.2. Embryonic Stage (2006–2010)
3.1.3. Initial Stage (2011–2015)
3.1.4. Developmental Stage (2016–2020)
3.2. Interpretations of QAP Results
4. Discussion
5. Conclusions
- (1)
- Between 2006 and 2020, the number of entities within the chili pepper industry cluster social network in Xinfu District increased from 9 to 76, and the overall network density declined from 0.236 to 0.064. The network structure became increasingly integrated and evolved a clear network center.
- (2)
- Based on social network analysis and confirmed by actual research information, this study divides the development history of the cluster into three stages: the embryonic stage (2006–2010), the initial stage (2011–2015), and the developmental stage (2016–2020). During the embryonic stage, an industry mode centered on brokers was formed, while, during the initial stage, a cooperative-centered mode appeared. Then, in the development stage, a mode centered on the chili association and leading enterprises was formed.
- (3)
- Labor exerted a positive effect on all three stages; however, its influence waned, declining from a coefficient of 0.297 during the embryonic stage to 0.122 and 0.102 in the initial and developmental stages, respectively, representing a decrease of 0.195. Meanwhile, the impact coefficients of capital and market declined from 0.326 and 0.309 in the embryonic stage to 0.127 and 0.232 in the initial stage. Policy support, however, demonstrated greater potency in the developmental stage, with its coefficient rising from 0.084 to 0.232. The impact of external capital remained relatively consistent across both stages, being 0.135 and 0.132. Technology, on the other hand, emerged as a hindrance in the developmental stage, with a negative impact coefficient of −0.102.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Wang, H.; Zhuo, Y. The necessary way for the development of China’s rural areas in the new era-rural revitalization strategy. Open J. Soc. Sci. 2018, 6, 97–106. [Google Scholar] [CrossRef] [Green Version]
- Li, E. The formation, evolution and innovative development of agricultural clusters in China: Case of the cluster nature of “Shouguang Mode”. Sci. Geogr. Sin. 2020, 40, 617–627. [Google Scholar]
- Rizwan, M.; Ali, S.; Adrees, M.; Ibrahim, M.; Tsang, D.C.; Zia-ur-Rehman, M.; Zahir, Z.A.; Rinklebe, J.; Tack, F.M.; Ok, Y.S. A critical review on effects, tolerance mechanisms and management of cadmium in vegetables. Chemosphere 2017, 182, 90–105. [Google Scholar] [CrossRef]
- Constantin, M.; Sacală, M.D.; Dinu, M.; Piștalu, M.; Pătărlăgeanu, S.R.; Munteanu, I.D. Vegetable Trade Flows and Chain Competitiveness Linkage Analysis Based on Spatial Panel Econometric Modelling and Porter’s Diamond Model. Agronomy 2022, 12, 411. [Google Scholar] [CrossRef]
- National Development and Reform Commission MOAA. National vegetable industry development plan. China Veg. 2012, 1. Available online: https://www.moa.gov.cn/nybgb/2012/dsanq/201805/t20180512_6141970.htm (accessed on 10 February 2023).
- Zou, Z.; Zou, X. Geographical and ecological differences in pepper cultivation and consumption in China. Front. Nutr. 2021, 742. [Google Scholar] [CrossRef]
- Simmel, G. The Sociology of Georg Simmel; Simon and Schuster: New York, NY, USA, 1950. [Google Scholar]
- Wellman, B.; Rainie, L. Networked: The New Social Operating System. New Media Soc. 2012. [Google Scholar] [CrossRef]
- Freeman, L. The development of social network analysis. Study Sociol. Sci. 2004, 1, 159–167. [Google Scholar]
- Borsboom, D.; Deserno, M.K.; Rhemtulla, M.; Epskamp, S.; Fried, E.I.; McNally, R.J.; Robinaugh, D.J.; Perugini, M.; Dalege, J.; Costantini, G.; et al. Network analysis of multivariate data in psychological science. Nat. Rev. Methods Prim. 2021, 1, 58. [Google Scholar] [CrossRef]
- Taylor, S.M.M.R. Substance use and abuse, COVID-19-related distress, and disregard for social distancing: A network analysis. Addict. Behav. 2021, 114, 106754. [Google Scholar] [CrossRef]
- Valeri, M.; Baggio, R. Italian tourism intermediaries: A social network analysis exploration. Curr. Issues Tour. 2021, 24, 1270–1283. [Google Scholar] [CrossRef]
- Singh, D.K.; Nithya, N.; Rahunathan, L.; Sanghavi, P.; Vaghela, R.S.; Manoharan, P.; Hamdi, M.; Tunze, G.B. Social network analysis for precise friend suggestion for twitter by associating multiple networks using ml. Int. J. Inf. Technol. Web Eng. 2022, 17, 1–11. [Google Scholar] [CrossRef]
- Litwin, H.; Levinsky, M. Social networks and mental health change in older adults after the Covid-19 outbreak. Aging Ment. Health 2022, 26, 925–931. [Google Scholar] [CrossRef]
- Porter, M.E. The Competitive Advantage of Nations; Harvard Business Review: Brighton, MA, USA, 1990. [Google Scholar]
- Feser, E.J.; Bergman, E.M. National industry cluster templates: A framework for applied regional cluster analysis. Reg. Stud. 2000, 34, 1–19. [Google Scholar] [CrossRef]
- Feldman, M.; Francis, J.; Bercovitz, J. Creating a cluster while building a firm: Entrepreneurs and the formation of industrial clusters. Reg. Stud. 2005, 39, 129–141. [Google Scholar] [CrossRef]
- Kiminami, L.; Nakamura, T. Food Security and Industrial Clustering in Northeast Asia; Springer: Berlin/Heidelberg, Germany, 2016. [Google Scholar]
- Doronina, I.I.; Borobov, V.N.; Ivanova, E.A.; Gorynya, E.V.; Zhukov, B.M. Agro-industrial clusters as a factor of increasing competitiveness of the region. Int. J. Econ. Financ. Issues 2016, 6, 295–299. [Google Scholar]
- Yang, F. The Construction and Competitiveness of Operation Model of Agricultural Industrial Cluster. Asian Agric. Res. 2011, 3, 80–84. [Google Scholar]
- Zhao, L.; Ruan, J.; Shi, X. Local industrial policies and development of agricultural clusters: A case study based on a tea cluster in China. Int. Food Agribus. Manag. Rev. 2021, 24, 267–288. [Google Scholar] [CrossRef]
- Seok, H.; Barnett, G.A.; Nam, Y. A social network analysis of international tourism flow. Qual. Quant. 2021, 55, 419–439. [Google Scholar] [CrossRef]
- Barrat, A.; Weigt, M. On the properties of small-world network models. Eur. Phys. J. B-Condens. Matter Complex Syst. 2000, 13, 547–560. [Google Scholar] [CrossRef] [Green Version]
- Kang, S.; Kim, W.G.; Park, D. Understanding tourist information search behaviour: The power and insight of social network analysis. Curr. Issues Tour. 2021, 24, 403–423. [Google Scholar] [CrossRef]
- Yang, G.; Gong, G.; Gui, Q. Exploring the Spatial Network Structure of Agricultural Water Use Efficiency in China: A Social Network Perspective. Sustainability 2022, 14, 2668. [Google Scholar] [CrossRef]
- Li, Y.; Luo, W. Influencing Factors of Knowledge Cooperation in Urban Agglomeration on Yangtze River Delta from the Perspective of Innovation Network; IOP Publishing: Bristol, UK, 2021. [Google Scholar]
- Li, Q.; Huang, J.; Luo, R.; Liu, C. China’s labor transition and the future of China’s rural wages and employment. China World Econ. 2013, 21, 4–24. [Google Scholar] [CrossRef]
- Wang, F.; Mason, A. Demographic Dividend and Prospects for Economic Development in China; U.N.: New York, NY, USA, 2007. [Google Scholar]
- Sui, F.; Yang, Y.; Zhao, S. What Affects the Production Technology of Labor-Intensive Agricultural Industries in the Context of Labor Aging? An Empirical Study Based on the Garlic Production in Lanling. Sustainability 2021, 14, 48. [Google Scholar] [CrossRef]
- Liu, J.; Zhang, C.; Hu, R.; Zhu, X.; Cai, J. Aging of agricultural labor force and technical efficiency in tea production: Evidence from Meitan County, China. Sustainability 2019, 11, 6246. [Google Scholar] [CrossRef] [Green Version]
- Lina, S. Function and Development of Agricultural Brokers. J. Anhui Agric. Sci. 2007, 35, 5313. [Google Scholar]
- Huang, Z.; Liang, Q. Agricultural organizations and the role of farmer cooperatives in China since 1978: Past and future. China Agric. Econ. Rev. 2018. [Google Scholar] [CrossRef]
- Bryzhko, V.G.; Kosheleva, L.A. The conceptual approach to managing the development of agricultural enterprises in the region. World Appl. Sci. J. 2012, 18, 191–196. [Google Scholar]
- Yang, Y.; Li, E. A theoretical framework and empirical analysis of the formation mechanism of green agricultural industry cluster: A case study of the Shouguang vegetable industry cluster in Shandong Province. Resour. Sci. 2021, 43, 69–81. [Google Scholar] [CrossRef]
1. Pepper Association | 2. WMCX | 3. CX | 4. HTY | 5. WHS | 6. CS | 7. KP | 8. HYL |
---|---|---|---|---|---|---|---|
9. ZJ | 10. CY | 11. WM | 12. HBNF | 13. MS | 14. SY | 15. HuiF | 16. HY |
17. AM | 18. QS | 19. FTM | 20. TDH | 21. QEH | 22. JS | 23. JY | 24. XY |
25. TTX | 26. XSY | 27. JM | 28. QR | 29. WXN | 30. LP | 31. SD | 32. LLXM |
33. TD | 34. CSL | 35. XDF | 36. XP | 37. BLT | 38. HYN | 39. WL | 40. YH |
41. LT | 42. PH | 43. CYN | 44. JYW | 45. JJ | 46. BY | 47. JX | 48. LQ |
49. Hyun | 50. HYT | 51. YS | 52. SX | 53. HJ | 54. HL | 55. SZM | 56. CSZZ |
57. LY | 58. GYH | 59. SH | 60. SL | 61. EX | 62. JW | 63. XT | 64. NXF |
65. JYF | 66. SC | 67. AW | 68. RF | 69. QJX | 70. DY | 71. JFT | 72. HF |
73. GZ | 74. GY | 75. QX | 76. YW |
Index | 2010 Numeric Value | 2015 Numeric Value | 2020 Numeric Value |
---|---|---|---|
Number of subject | 9 | 30 | 76 |
Agglomeration coefficient | 0.157 | 0.298 | 0.470 |
Network density | 0.236 | 0.236 | 0.064 |
Average path length | 2.063 | 2.010 | 2.739 |
Years | Index | Normalize Regression Coefficients | Significance Test |
---|---|---|---|
2010 | Technology | 0.212 | 0.078 |
Policy | −0.094 | 0.477 | |
Labor | 0.297 | 0.004 | |
Capital | 0.326 | 0.003 | |
Market | 0.309 | 0.006 | |
External capital | −0.179 | 0.120 | |
2015 | Technology | −0.010 | 0.411 |
Policy | 0.084 | 0.035 | |
Labor | 0.122 | 0.004 | |
Fund | 0.095 | 0.018 | |
Market | 0.162 | 0.001 | |
External capital | 0.135 | 0.002 | |
2020 | Technology | −0.102 | 0.008 |
Policy | 0.232 | 0.000 | |
Labor | 0.102 | 0.014 | |
Fund | 0.127 | 0.004 | |
Market | 0.232 | 0.000 | |
External capital | 0.132 | 0.003 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Yu, J.; You, F.; Wang, J.; Wang, Z. Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province. Sustainability 2023, 15, 4948. https://doi.org/10.3390/su15064948
Yu J, You F, Wang J, Wang Z. Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province. Sustainability. 2023; 15(6):4948. https://doi.org/10.3390/su15064948
Chicago/Turabian StyleYu, Jie, Fei You, Jian Wang, and Zishan Wang. 2023. "Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province" Sustainability 15, no. 6: 4948. https://doi.org/10.3390/su15064948
APA StyleYu, J., You, F., Wang, J., & Wang, Z. (2023). Evolution Modes of Chili Pepper Industry Clusters under the Perspective of Social Network—An Example from Xinfu District, Xinzhou, Shanxi Province. Sustainability, 15(6), 4948. https://doi.org/10.3390/su15064948