Skip to main content
SpringerLink
Log in

Investigating the spatial foundations of rural entrepreneurship development using a hybrid method of MCDM, ANN and DTree algorithm

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Growing evidence points to rural entrepreneurship as a critical tactic for promoting capacity building, sustainable development, and empowerment in rural communities. This study explored the spatial underpinnings of rural entrepreneurial development and was carried out in Iran. Using a descriptive-analytical methodology, the study surveys 20 experts and 100 active entrepreneurs in the Mochesh district of Kamyaran town. The research uses semi-structured interviews and sources to determine important characteristics for examining the spatial underpinnings. Data were collected using a researcher-made close-ended questionnaire. The validity and reliability of the research instrument were approved by experts opinions and Cronbach’s alpha coefficients, respectively. Analytical techniques used include decision tree data mining models, artificial neural networks (ANN), and Multi-Criteria Decision Making models. The results of the ANN model have shown that social and economic capital had been of the greatest importance in enhancing entrepreneur contexts, with a coefficient of 21 and 22%, respectively. Also, the findings of the data mining analysis show the high impact of environmental capital and structural-spatial components in the development of entrepreneurial activities in the study area. Finally, the entrepreneurs emphasized that one of the most important dimensions affecting the development of entrepreneurial activities and the prosperity of the rural economy is environmental capital and attracting the support of administrative institutions. Therefore, improvement in these components should be duly considered by managers and rural planners.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

(Source: Ahmadi Dehrashid, 2022)

Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The data supporting this study's findings are available from the corresponding author upon reasonable request.

References

  • Akgün, A.y.A., Baycan-Levent, T.n., Nijkamp, P., Poot, J. (2011). Roles of local and newcomer entrepreneurs in rural development: A comparative meta-analytic study. Regional Studies, 45(9), 1207–1223. https://doi.org/10.1080/00343401003792500

    Article  Google Scholar 

  • Agarwal, S., Rahman, S., & Errington, A. (2009). Measuring the determinants of relative economic performance of rural areas. Journal of Rural Studies, 25(3), 309-321.

  • Azar, A. T., & El-Metwally, S. M. (2013). Decision tree classifiers for automated medical diagnosis. Neural Computing and Applications, 23, 2387–2403.

  • Barth, H., & Zalkat, G. (2021). Refugee entrepreneurship in the agri-food industry: The Swedish experience. Journal of Rural Studies, 86, 189–197. https://doi.org/10.1016/j.jrurstud.2021.06.011

    Article  Google Scholar 

  • Baumgartner, D., Schulz, T., & Seidl, I. (2013). Quantifying entrepreneurship and its impact on local economic performance: A spatial assessment in rural Switzerland. Entrepreneurship & Regional Development, 25(3–4), 222–250. https://doi.org/10.1080/08985626.2012.710266

    Article  Google Scholar 

  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  • Chamoli, S. (2015). Hybrid FAHP (fuzzy analytical hierarchy process)-FTOPSIS (fuzzy technique for order preference by similarity of an ideal solution) approach for performance evaluation of the V down perforated baffle roughened rectangular channel. Energy, 84, 432–442. https://doi.org/10.1016/j.energy.2015.03.007

    Article  Google Scholar 

  • Chen, C., & Pan, J. (2019). The effect of the health poverty alleviation project on financial risk protection for rural residents: Evidence from Chishui City, China. International Journal for Equity in Health, 18(1), 1–16.

    Article  Google Scholar 

  • Chen, W., Wang, B., Chen, Y., Zhang, J., & Xiao, Y. (2023). New exploration of creativity: Cross-validation analysis of the factors influencing multiteam digital creativity in the transition phase. Frontiers in Psychology, 14, 1102085.

    Article  Google Scholar 

  • Chen, Z., & Yang, W. (2011). An MAGDM based on constrained FAHP and FTOPSIS and its application to supplier selection. Mathematical and Computer Modelling, 54(11), 2802–2815. https://doi.org/10.1016/j.mcm.2011.06.068

    Article  Google Scholar 

  • Corrêa, V. S., Abreu, A. P. P. C., Vivaldini, M., & Cruz, M.d.A. (2023). Influence of social and spatial embeddedness on rural entrepreneurship in the Amazon: A study with a Brazilian tribe’ enterprising Indians. Journal of Place Management and Development, 16(3), 388–414. https://doi.org/10.1108/JPMD-10-2022-0095

    Article  Google Scholar 

  • del Olmo-García, F., Domínguez-Fabián, I., Crecente-Romero, F. J., & del Val-Núñez, M. T. (2023). Determinant factors for the development of rural entrepreneurship. Technological Forecasting and Social Change, 191, 122487. https://doi.org/10.1016/j.techfore.2023.122487

    Article  Google Scholar 

  • Deller, S., Kures, M., & Conroy, T. (2019). Rural entrepreneurship and migration. Journal of Rural Studies, 66, 30–42. https://doi.org/10.1016/j.jrurstud.2019.01.026

    Article  Google Scholar 

  • Dong, J., Xu, W., & Cha, J. (2021). Rural entrepreneurship and job creation: The hybrid identity of village-cadre-entrepreneurs. China Economic Review, 70, 101704. https://doi.org/10.1016/j.chieco.2021.101704

    Article  Google Scholar 

  • Ghani, E., Kerr, W.R., O'connell, S., 2017. Spatial determinants of entrepreneurship in India, In: Entrepreneurship in a Regional Context. Routledge, pp. 133–151.

  • Ghani, E., Kerr, W. R., & O’Connell, S. (2014). Spatial Determinants of Entrepreneurship in India. Regional Studies, 48(6), 1071–1089. https://doi.org/10.1080/00343404.2013.839869

    Article  Google Scholar 

  • Güzel, Ö., Ehtiyar, R., & Ryan, C. (2021). The Success Factors of wine tourism entrepreneurship for rural area: A thematic biographical narrative analysis in Turkey. Journal of Rural Studies, 84, 230–239. https://doi.org/10.1016/j.jrurstud.2021.04.021

    Article  Google Scholar 

  • Ho, T.K., 1995. Random decision forests, In: Proceedings of 3rd international conference on document analysis and recognition. IEEE, pp. 278–282.

  • Klege, R. A., Visser, M., Barron, A., & M.F., Clarke, R.P.,. (2021). Competition and gender in the lab vs field: Experiments from off-grid renewable energy entrepreneurs in Rural Rwanda. Journal of Behavioral and Experimental Economics, 91, 101662. https://doi.org/10.1016/j.socec.2021.101662

    Article  Google Scholar 

  • Koehne, F., Woodward, R., & Honig, B. (2022). The potentials and perils of prosocial power: Transnational social entrepreneurship dynamics in vulnerable places. Journal of Business Venturing, 37(4), 106206. https://doi.org/10.1016/j.jbusvent.2022.106206

    Article  Google Scholar 

  • Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39(4), 261–283. https://doi.org/10.1007/s10462-011-9272-4

    Article  Google Scholar 

  • Lange, A., Piorr, A., Siebert, R., & Zasada, I. (2013). Spatial differentiation of farm diversification: How rural attractiveness and vicinity to cities determine farm households’ response to the CAP. Land Use Policy, 31, 136–144. https://doi.org/10.1016/j.landusepol.2012.02.010

    Article  Google Scholar 

  • Li, B., Li, G., & Luo, J. (2021). Latent but not absent: The ‘long tail’nature of rural special education and its dynamic correction mechanism. PLoS ONE, 16(3), e0242023.

    Article  CAS  Google Scholar 

  • Liang, X., & Meng, X. (2019). An extended FTOPSIS method for freeway route selection in the pre-feasibility study stage. Physica a: Statistical Mechanics and Its Applications, 526, 120871. https://doi.org/10.1016/j.physa.2019.04.107

    Article  Google Scholar 

  • Liu, J., Zhong, D., Liu, J., & Liao, Z. (2023). B&B accommodation entrepreneurship in rural China: How does embeddedness make a difference? Journal of Hospitality and Tourism Management, 56, 284–294. https://doi.org/10.1016/j.jhtm.2023.06.021

    Article  Google Scholar 

  • Luo, J., Zhao, C., Chen, Q., & Li, G. (2022). Using deep belief network to construct the agricultural information system based on Internet of Things. The Journal of Supercomputing, 78(1), 379–405. https://doi.org/10.1007/s11227-021-03898-y

    Article  Google Scholar 

  • Luo, J., Zhuo, W., & Xu, B. (2023). The bigger, the better? Optimal NGO size of human resources and governance quality of entrepreneurship in circular economy. Management Decision. https://doi.org/10.1108/md-03-2023-0325

    Article  Google Scholar 

  • Mashapure, R., Nyagadza, B., Chikazhe, L., Msipa, N., Ngorora, G. K. P., & Gwiza, A. (2022). Challenges hindering women entrepreneurship sustainability in rural livelihoods: Case of Manicaland province. Cogent Social Sciences, 8(1), 2132675. https://doi.org/10.1080/23311886.2022.2132675

    Article  Google Scholar 

  • Miao, S., Chi, J., Liao, J., & Qian, L. (2021). How does religious belief promote farmer entrepreneurship in rural China? Economic Modelling, 97, 95–104. https://doi.org/10.1016/j.econmod.2021.01.015

    Article  Google Scholar 

  • Miles, M. P., & Morrison, M. (2020). An effectual leadership perspective for developing rural entrepreneurial ecosystems. Small Business Economics, 54(4), 933–949. https://doi.org/10.1007/s11187-018-0128-z

    Article  Google Scholar 

  • Mishra, R., Shiradkar, S., Werner, K., Maria, T., Kumar, P., Venkateswaran, J., & Solanki, C. S. (2023). Dynamics of solar energy entrepreneurship in rural Bihar. India. Energy for Sustainable Development, 76, 101269. https://doi.org/10.1016/j.esd.2023.101269

    Article  Google Scholar 

  • Müller, S., & Korsgaard, S. (2018). Resources and bridging: The role of spatial context in rural entrepreneurship. Entrepreneurship & Regional Development, 30(1–2), 224–255. https://doi.org/10.1080/08985626.2017.1402092

    Article  Google Scholar 

  • Nijkamp, P. (2011). Entrepreneurship, development and the spatial context: retrospect and prospects. In W. Naudé (Ed.), Entrepreneurship and economic development (pp. 271–293). Palgrave Macmillan UK.

    Chapter  Google Scholar 

  • Nordbø, I. (2022). Female entrepreneurs and path-dependency in rural tourism. Journal of Rural Studies, 96, 198–206. https://doi.org/10.1016/j.jrurstud.2022.09.032

    Article  Google Scholar 

  • Pelz, S., Pachauri, S., & Falchetta, G. (2023). Short-run effects of grid electricity access on rural non-farm entrepreneurship and employment in Ethiopia and Nigeria. World Development Perspectives, 29, 100473. https://doi.org/10.1016/j.wdp.2022.100473

    Article  Google Scholar 

  • Qu, M., & Zollet, S. (2023). Neo-endogenous revitalisation: Enhancing community resilience through art tourism and rural entrepreneurship. Journal of Rural Studies, 97, 105–114. https://doi.org/10.1016/j.jrurstud.2022.11.016

    Article  Google Scholar 

  • Romero-Castro, N., López-Cabarcos, M. A., & Piñeiro-Chousa, J. (2023). Finance, technology, and values: A configurational approach to the analysis of rural entrepreneurship. Technological Forecasting and Social Change, 190, 122444. https://doi.org/10.1016/j.techfore.2023.122444

    Article  Google Scholar 

  • Sahrakorpi, T., & Bandi, V. (2021). Empowerment or employment? uncovering the paradoxes of social entrepreneurship for women via husk power systems in rural North India. Energy Research & Social Science, 79, 102153. https://doi.org/10.1016/j.erss.2021.102153

    Article  Google Scholar 

  • Saridakis, G., Georgellis, Y., Muñoz Torres, R. I., Mohammed, A.-M., & Blackburn, R. (2021). From subsistence farming to agribusiness and nonfarm entrepreneurship: Does it improve economic conditions and well-being? Journal of Business Research, 136, 567–579. https://doi.org/10.1016/j.jbusres.2021.07.037

    Article  Google Scholar 

  • Shang, Y., Song, K., Lai, F., Lyu, L., Liu, G., Fang, C., Hou, J., Qiang, S., Yu, X., & Wen, Z. (2023). Remote sensing of fluorescent humification levels and its potential environmental linkages in lakes across China. Water Research, 230, 119540. https://doi.org/10.1016/j.watres.2022.119540

    Article  CAS  Google Scholar 

  • Shao, K., Ma, R., & Kamber, J. (2023). An in-depth analysis of the entrepreneurship of rural Chinese mothers and the digital inclusive finance. Telecommunications Policy, 47(7), 102593. https://doi.org/10.1016/j.telpol.2023.102593

    Article  Google Scholar 

  • Shrivastava, U., & Kumar Dwivedi, A. (2021). Manifestations of rural entrepreneurship: The journey so far and future pathways. Management Review Quarterly, 71(4), 753–781. https://doi.org/10.1007/s11301-020-00199-1

    Article  Google Scholar 

  • Soleymani, A., YaghoubiFarani, A., Karimi, S., Azadi, H., Nadiri, H., & Scheffran, J. (2021). Identifying sustainable rural entrepreneurship indicators in the Iranian context. Journal of Cleaner Production, 290, 125186. https://doi.org/10.1016/j.jclepro.2020.125186

    Article  Google Scholar 

  • Trettin, L., & Welter, F. (2011). Challenges for spatially oriented entrepreneurship research. Entrepreneurship & Regional Development, 23(7–8), 575–602. https://doi.org/10.1080/08985621003792988

    Article  Google Scholar 

  • Trigkas, M., Partalidou, M., & Lazaridou, D. (2021). Trust and other historical proxies of social capital: Do they matter in promoting social entrepreneurship in greek rural areas? Journal of Social Entrepreneurship, 12(3), 338–357. https://doi.org/10.1080/19420676.2020.1718741

    Article  Google Scholar 

  • Vettehen, P. H., & Schaap, G. (2023). An attention economic perspective on the future of the information age. Futures, 153, 103243. https://doi.org/10.1016/j.futures.2023.103243

    Article  Google Scholar 

  • Wang, Y., Jiang, Y., Geng, B., Wu, B., & Liao, L. (2022). Determinants of returnees’ entrepreneurship in rural marginal China. Journal of Rural Studies, 94, 429–438. https://doi.org/10.1016/j.jrurstud.2022.07.014

    Article  Google Scholar 

  • Xiao, W., & Wu, M. (2021). Life-cycle factors and entrepreneurship: Evidence from rural China. Small Business Economics, 57(4), 2017–2040. https://doi.org/10.1007/s11187-020-00370-8

    Article  Google Scholar 

  • Xu, A., Qiu, K., & Zhu, Y. (2023). The measurements and decomposition of innovation inequality: Based on Industry − University − Research perspective. Journal of Business Research, 157, 113556. https://doi.org/10.1016/j.jbusres.2022.113556

    Article  Google Scholar 

  • Xu, F., He, X., & Yang, X. (2021). A multilevel approach linking entrepreneurial contexts to subjective well-being: Evidence from Rural Chinese entrepreneurs. Journal of Happihttps://doi.org/10.1038/s41598-019-39015-6ness Studies, 22(4), 1537–1561. https://doi.org/10.1007/s10902-020-00283-z

    Article  Google Scholar 

  • Deng, X., Li, L., Enomoto, M., et al. (2019). Continuously frequency-tuneable plasmonic structures for terahertz bio-sensing and spectroscopy. Scientific Reports, 9, 3498. https://doi.org/10.1038/s41598-019-39015-6

    Article  CAS  Google Scholar 

  • Deng, X., Simanullang, M., & Kawano, Y. (2018). Ge-core/a-si-shell nanowire-based field-effect transistor for sensitive terahertz detection. Photonics, 5(2), 13.

    Article  CAS  Google Scholar 

  • Deng, X., & Kawano, Y. (2018a). Surface plasmon polariton graphene midinfrared photodetector with multifrequency resonance. Journal of Nanophotonics, 12(2), 026017–026017.

    Article  Google Scholar 

  • Deng, X., Hu, Z., Xiu, G., Li, D., Yue, Y, Song, Z., Weng, Z., Xu, J., Wang, Z. (2010). Five-beam interference pattern model for laser interference lithography. In The 2010 IEEE international conference on information and automation, (pp. 1208–1213).

  • Deng, X., Oda, S., & Kawano, Y. (2016a). Frequency selective, high transmission spiral terahertz plasmonic antennas. Journal of Modeling and Simulation of Antennas and Propagation, 2, 1–6.

    Google Scholar 

  • Deng, X., & Kawano, Y. (2018b). Terahertz plasmonics and nano-carbon electronics for nano-micro sensing and imaging. International Journal of Automation Technology, 12(1), 87–96.

    Article  Google Scholar 

  • Deng, X., Oda, S., Kawano, Y. (2016). Split-joint bull's eye structure with aperture optimization for multi-frequency terahertz plasmonic antennas. In: 2016 41st International conference on infrared, millimeter, and terahertz waves, (pp. 1–2).

  • Deng, X., Dong, Z., Ma, X., Wu, H., Wang, B., Du, X. (2009a). Exploration on mechanics design for scanning tunneling microscope. In: 2009 Symposium on Photonics and Optoelectronics, (pp. 1–4), IEEE.

  • Moayedi, H., & Dehrashid, A. A. (2023). A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility mapping. Environmental Science and Pollution Research, 30(34), 82964–82989.

    Article  Google Scholar 

  • Sugaya, T., Deng, X. (2019). Resonant frequency tuning of terahertz plasmonic structures based on solid immersion method. In 2019 44th international conference on infrared, millimeter, and terahertz waves, 1–2.

  • Deng, X., Dong, Z., Ma, X., Wu, H., & Wang, B. (2009b). Active gear-based approach mechanism for scanning tunneling microscope. International Conference on Mechatronics and Automation, 2009, 1317–1321.

    Google Scholar 

  • Kong, C., Zhu, H., Li, H., Liu, J., Wang, Z., Qian, Y. (2019a). Multi-agent negotiation in real-time bidding. In IEEE international conference on consumer electronics-Taiwan (ICCE-TW), 1–2

  • Kong, C., Liu, J., Li, H., Liu, Y., Zhu, H., Liu, T. (2019b). Drug abuse detection via broad learning. In Web information systems and applications: 16th international conference, WISA 2019, Qingdao, China, (September 20–22, 2019, Proceedings 16).

  • Kong, C., Li, H., Zhu, H., Xiu, Y., Liu, J., Liu, T. (2019c). Anonymized user linkage under differential privacy. In Soft computing in data science: 5th international conference, SCDS 2019, Iizuka, Japan, August 28–29, 2019, Proceedings 5.

  • Zhou, Y., Osman, A., Willms, M., Kunz, A., Philipp, S., Blatt, J., Eul, S. (2023). Semantic wireframe detection. Ndt.net DGZfP

  • Wang, H., Zhou, Y., Perez, E., Roemer, F. (2024). Jointly learning selection matrices for transmitters, receivers and fourier coefficients In Multichannel imaging. ICASSP.

  • Zhu, H., Wang, B. (2021). Negative siamese network for classifying semantically similar sentences. In International conference on Asian language processing (IALP), (pp. 170–173).

  • Kong, C., Li, H., Zhang, L., Zhu, H., Liu, T. (2019d). Link prediction on dynamic heterogeneous information networks. In International conference on computational data and social networks, (pp. 339–350).

  • Dai, W. (2021). Safety evaluation of traffic system with historical data based on markov process and deep-reinforcement learning. Journal of Safety Evaluation of Traffic System with Historical Data, 1–14.

  • Dai, W. (2022). Evaluation and improvement of carrying capacity of a traffic system. Innovations in Applied Engineering and Technology. https://doi.org/10.58195/iaet.v1i1.001

    Article  Google Scholar 

  • Dai, W. (2023). Design of traffic improvement plan for line 1 Baijiahu station of Nanjing metro. Innovations in Applied Engineering and Technology. https://doi.org/10.58195/iaet.v2i1.133

    Article  Google Scholar 

  • Wenjun, D., Fatahizadeh, M., Touchaei, H. G., Moayedi, H., & Foong, L. K. (2023). Application of six neural network-based solutions on bearing capacity of shallow footing on double-layer soils. Steel and Composite Structures, 49(2), 231–244. https://doi.org/10.12989/scs.2023.49.2.231

    Article  Google Scholar 

  • Zhang, Y., Abdullah, S., Ullah, I., & Ghani, F. (2024). A new approach to neural network via double hierarchy linguistic information: Application in robot selection. Engineering Applications of Artificial Intelligence, 129, 107581. https://doi.org/10.1016/j.engappai.2023.107581

    Article  Google Scholar 

  • Zhang, Y., Gono, R., & Jasiński, M. (2023). an improvement in dynamic behavior of single phase PM brushless DC motor using deep neural network and mixture of experts. IEEE Access, 12, 64260–64271. https://doi.org/10.1109/ACCESS.2023.3289409

    Article  Google Scholar 

  • Zhang, Y., & Zhang, H. (2023). Enhancing robot path planning through a twin-reinforced chimp optimization algorithm and evolutionary programming algorithm. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3337602

    Article  Google Scholar 

  • Zhao, Y., Dai, W., Wang, Z., & Ragab, A. E. (2023). Application of computer simulation to model transient vibration responses of GPLs reinforced doubly curved concrete panel under instantaneous heating. Materials Today Communications, 107, 949. https://doi.org/10.1016/j.mtcomm.2023.107949

    Article  CAS  Google Scholar 

  • Li, L. (2023). An empirical analysis of rural labor transfer and household income growth in China. Journal of Chinese Human Resources Management, 14(1), 106–116. https://doi.org/10.47297/wspchrmWSP2040-800505.20231401

    Article  Google Scholar 

  • Ikram, R. M. A., Dehrashid, A. A., Zhang, B., Chen, Z., Le, B. N., & Moayedi, H. (2023). A novel swarm intelligence: Cuckoo optimization algorithm (COA) and SailFish optimizer (SFO) in landslide susceptibility assessment. Stochastic Environmental Research and Risk Assessment, 37(5), 1717–1743.

    Article  Google Scholar 

  • Adnan Ikram, R. M., Khan, I., Moayedi, H., Ahmadi Dehrashid, A., Elkhrachy, I., & Nguyen Le, B. (2023). Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environment, Development and Sustainability, 1–33.

  • Sun, Y., Dai, H. L., Xu, L., Asaditaleshi, A., Ahmadi Dehrashid, A., Adnan Ikram, R. M., ... & Thi, Q. T. (2023). Development of the artificial neural network’s swarm-based approaches predicting East Azerbaijan landslide susceptibility mapping. Environment, Development and Sustainability, 1–38.

  • Ahmadi Dehrashid, A., Bijani, M., Valizadeh, N., Ahmadi Dehrashid, H., Nasrollahizadeh, B., & Mohammadi, A. (2021). Food security assessment in rural areas: Evidence from Iran. Agriculture & Food Security, 10(1), 17.

    Article  Google Scholar 

  • Shen, Y., Ahmadi Dehrashid, A., Bahar, R. A., Moayedi, H., & Nasrollahizadeh, B. (2023). A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran. Environmental Science and Pollution Research, 30(59), 123527–123555.

    Article  Google Scholar 

  • Ahmadi Dehrashid, A. (2022). Economic development and rural employment creation pan of Kamyaran town, University of Kurdistan, Kurdistan Studies Research Institute, Number: P. K. 7238. (In Persian)

Download references

Acknowledgements

We want to express our appreciation to all the participants, without whom this study would be impossible.

Author information

Authors and Affiliations

  1. School of Safety and Management, Zhejiang Institute of Security Technology, Wenzhou City, Zhejiang Province, China

    Dandan Ye

  2. Department of Human Geography, Faculty of Geography, University of Tehran, Tehran, Iran

    Hossein Ahmadi Dehrashid

  3. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

  4. School of Engineering and Technology, Duy Tan University, Da Nang, Vietnam

    Hossein Moayedi

  5. Department of Climatology, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran

    Atefeh Ahmadi Dehrashid

  6. Department of Zrebar Lake Economic – Social Research, Kurdistan Studies Institute, University of Kurdistan, Sanandaj, Iran

    Atefeh Ahmadi Dehrashid

Authors
  1. Dandan Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Ahmadi Dehrashid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hossein Moayedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Atefeh Ahmadi Dehrashid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hossein Moayedi or Atefeh Ahmadi Dehrashid.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

All ethical responsibilities are considered regarding the publication of this paper.

Consent to participate

All authors have participated in the final version of the manuscript.

Consent to publish

All authors have read and agreed to the published version of the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Figs. 10 and 11.

Fig. 10
figure 10

Scattering variables around the axis of dependent variable

Full size image
Fig. 11
figure 11

Spatial distribution of villages in the study area

Full size image

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, D., Ahmadi Dehrashid, H., Moayedi, H. et al. Investigating the spatial foundations of rural entrepreneurship development using a hybrid method of MCDM, ANN and DTree algorithm. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04739-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10668-024-04739-7

Keywords

Search

Navigation