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Journals / Tarım Bilimleri Dergisi / 2026 / Volume: 31 Issue: 3

Evaluation of Smart Agricultural Technologies Used in Smart Villages: SWARA Approach

Rukiye Gizem Öztaş Karlı(BARTIN ÜNİVERSİTESİ, MÜHENDİSLİK, MİMARLIK VE TASARIM FAKÜLTESİ, PEYZAJ MİMARLIĞI BÖLÜMÜ)0000-0003-0999-418X
Pages
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Publication Date
2025-07-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal that technological relevance (0.267) is the most significant criterion, followed by efficiency and performance (0.205), economic factors (0.164), environmental sustainability (0.137), social and user satisfaction (0.119), and political and governance factors (0.108).

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The rapid development of technology has paved the way for innovative solutions in agriculture, particularly in smart villages.
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Key Findings & Data:The findings reveal that technological relevance (0.267) is the most significant criterion, followed by efficiency and performance (0.205), economic factors (0.164), environmental sustainability (0.137), social and user satisfaction (0.119), and political and governance factors (0.108).
💡
Conclusion & Impact:Furthermore, it emphasizes the importance of stakeholder collaboration, locally specific strategies, and continuous adaptation of technologies to regional needs, ensuring the sustainable development of smart villages.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Agricultural technologies · smart agriculture · smart village

Abstract

The rapid development of technology has paved the way for innovative solutions in agriculture, particularly in smart villages. This study evaluates the effectiveness and sustainability of agricultural technologies used in smart villages by employing the SWARA (Stepwise Weight Assessment Ratio Analysis) method. Six main criteria and eighteen subcriteria were assessed based on expert evaluations. The findings reveal that technological relevance (0.267) is the most significant criterion, followed by efficiency and performance (0.205), economic factors (0.164), environmental sustainability (0.137), social and user satisfaction (0.119), and political and governance factors (0.108). Among the sub-criteria, innovativeness, yield increase, and cost effectiveness were identified as critical factors influencing the adoption of smart agricultural technologies. The study offers actionable recommendations, including prioritizing user-friendly and cost-effective technologies, enhancing financial incentives, and aligning policies with global sustainability goals such as SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production). Furthermore, it emphasizes the importance of stakeholder collaboration, locally specific strategies, and continuous adaptation of technologies to regional needs, ensuring the sustainable development of smart villages.

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Cite This Article

Karlı, R. G. Ö. (2025). Evaluation of Smart Agricultural Technologies Used in Smart Villages: SWARA Approach. Tarım Bilimleri Dergisi, Volume: 31 Issue: 3. https://doi.org/10.15832/ankutbd.1571732

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References (82)

  1. Abualkishik A Z, Almajed R & Thompson W (2022). Evaluating smart agricultural production efficiency using fuzzy MARCOS method. J. Neutrosophic Fuzzy Syst, 3:8-18. https://doi.org/10.54216/JNFS.030101
  2. Acemoglu D (2002). Technical change, inequality, and the labor market. Journal of Economic Literature 40(1): 7-72. https://doi.org/10.1257/0022051026976
  3. Acosta M, Riley S, Bonilla-Findji O, Martínez-Barón D, Howland F, Huyer S & Chanana N (2021). Exploring women’s differentiated access to climate-smart agricultural interventions in selected climate-smart villages of Latin America. Sustainability 13(19): 10951. https://doi.org/10.3390/su131910951
  4. Adewale C, Reganold J P, Higgins S, Evans R D & Carpenter-Boggs L (2019). Agricultural carbon footprint is farm specific: Case study of two organic farms. Journal of Cleaner Production 229: 795-805. https://doi.org/10.1016/j.jclepro.2019.04.253
  5. Adli H, Remli M A, Wan Salihin Wong K N S, Ismail N A, González-Briones A, Corchado J M & Mohamad M S (2023). Recent advancements and challenges of AIoT application in smart agriculture: A review. Sensors 23(7): 3752. https://doi.org/10.3390/s23073752
  6. Al-Ali A R, Al Nabulsi A, Mukhopadhyay S, Awal M S, Fernandes S & Ailabouni K (2019). IoT-solar energy powered smart farm irrigation system. Journal of Electronic Science and Technology 17(4): 100017. https://doi.org/10.1016/j.jnlest.2020.100017
  7. Al-Shareeda M A, Manickam S & Saare M A (2022). Intelligent drone-based IoT technology for smart agriculture system, 41-45. In 2022 International Conference on Data Science and Intelligent Computing (ICDSIC) (1-2 November 2022, Karbala), IEEE. https://doi.org/10.1109/ICDSIC56987.2022.10076170
  8. Amadu F O, McNamara P E & Miller D C (2020). Yield effects of climate-smart agriculture aid investment in southern Malawi. Food Policy 92: 101869. https://doi.org/10.1016/j.foodpol.2020.101869
  9. Anderson A, Loomba P, Orajaka I, Numfor J, Saha S, Janko S. & Larsen R (2017). Empowering smart communities: electrification, education, and sustainable entrepreneurship in IEEE Smart Village Initiatives. IEEE Electrification Magazine 5(2): 6-16. https://doi.org/10.1109/MELE.2017.2685738
  10. Ashraf A R, Thongpapanl N & Auh S (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing 22(3): 68-93. https://doi.org/10.1509/jim.14.0065
  11. Balafoutis A T, Evert F K V & Fountas S (2020). Smart farming technology trends: economic and environmental effects, labor impact, and adoption readiness. Agronomy 10(5): 743. https://doi.org/10.3390/agronomy10050743
  12. Baran E & Ersoy Karaçuha M. (2021). Adaptation to global climate change: Smart agricultural practices and occupational health and safety, 13-20. National Occupational Health and Safety Student Congress Proceedings Book (3-4 April 2021, İstanbul) (In Turkish)
  13. Barnes A P, Soto I, Eory V, Beck B, Balafoutis A, Sánchez B & Gómez-Barbero M (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy 80: 163-174. https://doi.org/10.1016/j.landusepol.2018.10.004
  14. Büyük A M, Ateş G, Burghli S, Yılmaz D, Temur G T & Sivri Ç (2021). Digital maturity assessment model for smart agriculture, pp. 289301. In Digital Conversion on the Way to Industry 4.0: Selected Papers from ISPR2020 (September 24-26, 2020 Turkey). Springer International Publishing. https://doi.org/10.1007/978-3-030-62784-3_24
  15. Büyüközkan G & Uztürk D (2024). Integrated design framework for smart agriculture: Bridging the gap between digitalization and sustainability. Journal of Cleaner Production 449: 141572. https://doi.org/10.1016/j.jclepro.2024.141572
  16. Carrer M J, de Souza Filho H M, Vinholis M D M B & Mozambani C I (2022). Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil. Technological Forecasting and Social Change 177: 121510. https://doi.org/10.1016/j.techfore.2022.121510
  17. Castiblanco Jimenez I A, Cepeda García L C, Violante M G, Marcolin F & Vezzetti E (2021). Commonly used external TAM variables in elearning, agriculture and virtual reality applications. Future Internet 13(1): 7. https://doi.org/10.3390/fi13010007
  18. Cesco S, Sambo P, Borin M, Basso B, Orze G & Mazzetto F (2023). Smart agriculture and digital twins: Applications and challenges in a vision of sustainability. European Journal of Agronomy 146: 126809. https://doi.org/10.1016/j.eja.2023.126809
  19. CGIAR (2022). Climate-smart village: The CCAFS model to improve the adaptive capacity of communities. (Web page: https://ccafs.cgiar.org/climate-smart-villages) (Data accessed: July 2024)
  20. Cornejo-Velazquez E, Clavel-Maqueda M, Acevedo-Sandoval O A & Romero-Trejo H (2022). Technological innovation strategy to strengthen the competitive advantages of smallholder farmers, 23-31. In: Innovation in Small-Farm Agriculture. 1st ed. (Eds. Rakshit, A., S. Chakraborty, M. Parihar, V.S. Meena, P.K. Mishra & H.B. Singh), CRC Press, 341 pp
  21. Cubero S, Marco-Noales E, Aleixos N, Barbé S & Blasco J (2020). Robhortic: A field robot to detect pests and diseases in horticultural crops by proximal sensing. Agriculture 10(7): 276. https://doi.org/10.3390/agriculture10070276
  22. Darnhofer I, Bellon S, Dedieu B & Milestad R (2010). Adaptiveness to enhance the sustainability of farming systems. A review. Agronomy for sustainable development 30: 545-555. https://doi.org/10.1051/agro/2009053
  23. Deichmann, U, Goyal A & Mishra D (2016). Will digital technologies transform agriculture in developing countries? Agricultural Economics, 47(S1): 21-33. https://doi.org/10.1111/agec.12300
  24. Del Río Castro G, González Fernández M C & Uruburu Colsa Á (2021). Unleashing the convergence amid digitalization and sustainability towards pursuing the Sustainable Development Goals (SDGs): A holistic review. Journal of Cleaner Production 280: 122204. https://doi.org/10.1016/j.jclepro.2020.122204
  25. Elijah O, Rahman T A, Orikumhi I. Leow C Y & Hindia M N (2018). An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of things Journal 5(5): 3758-3773. https://doi.org/10.1109/JIOT.2018.2844296
  26. Engler N & Krarti M (2021). Review of energy efficiency in controlled environment agriculture. Renewable and Sustainable Energy Reviews, 141: 110786. https://doi.org/10.1016/j.rser.2021.110786
  27. ENRD (2018). Smart villages: Revitalising rural services, EU Rural Review 26. (Web page: enrd.ec.europa.eu) (Data accessed: July 2024).
  28. Gabriel A & Gandorfer M (2023). Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region. Precision Agriculture, 24(1): 68-91. https://doi.org/10.1007/s11119-022-09931-1
  29. Gerli P, Navio Marco J & Whalley J (2022). What makes a smart village smart? A review of the literature. Transforming Government: People, Process and Policy 16(3): 292-304. https://doi.org/10.1108/TG-07-2021-0126
  30. Gobarah M E, Tawfik M M, Thalooth A T & Housini E A E (2015). Water conservation practices in agriculture to cope with water scarcity. International Journal of Water Resources and Arid Environments 4(1): 20-29
  31. Gorlov I F, Fedotova G V, Glushchenko A V, Slozhenkina M I & Mosolova N I (2020). Digital technologies in the development of the agroindustrial complex, 220-229. In: Digital Economy: Complexity and Variety vs. Rationality 9 (Eds. Popkova E & B. Sergi), Springer International Publishing. https://doi.org/10.1007/978-3-030-29586-8_26
  32. Htitiou A, Boudhar A, Lebrini Y & Benabdelouahab T (2020). Deep learning-based reconstruction of spatiotemporally fused satellite images for smart agriculture applications in a heterogeneous agricultural region. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 44: 249-254. https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-249-2020
  33. Jiang H, Feng J & Zhang Y (2015). Practice and thinking about the construction of beautiful and intelligent village: Take Xibaidian Village of Beijing city as an example,pp. 268–277. In Proceedings of the Annual Conference of China Society of Agricultural Resources and Regional Planning (23 July 2015, Xining, China).
  34. Jin P, Yu L, Ahmad K, Shafique H M & Ahmad A (2024). Evaluating the factors influencing the adoption of digital culture among university students in developing areas of South Punjab. Information Development, 02666669241270909. https://doi.org/10.1177/026666692412709
  35. Karunathilake E M B M, Le A T, Heo S, Chung Y S & Mansoor S (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture 13(8): 1593. https://doi.org/10.3390/agriculture13081593 Keršuliene V, Zavadskas E K & Turskis Z (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management 11(2): 243-258. https://doi.org/10.3846/jbem. 2010.12
  36. Kılavuz E & Erdem İ (2019). Agriculture 4.0 applications in the world and transformation of Turkish agriculture. Social Sciences 14(4): 133 157 (In Turkish)
  37. Kour V P & Arora S (2020). Recent developments of the internet of things in agriculture: A survey. IEEE Access, 8: 129924-129957. https://doi.org/10.1109/ACCESS.2020.3009298
  38. Kuhlmann K (2015). Harmonizing regional seed regulations in sub-Saharan Africa: A comparative assessment. Available at SSRN 4126687 http://dx.doi.org/10.2139/ssrn.4126687
  39. Lakhiar I A, Yan H, Zhang C, Wang G, He B, Hao B & Rakibuzzaman M (2024). A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints. Agriculture 14(7): 1141. https://doi.org/10.3390/agriculture14071141
  40. Makarem S C, Mudambi S M & Podoshen J S (2009). Satisfaction in technology‐enabled service encounters. Journal of Services Marketing 23(3): 134-144. https://doi.org/10.1108/08876040910955143
  41. Malche T & Maheshwary P. (2017). Internet of things (IoT) based water level monitoring system for smart village, pp. 305-312. In Proceedings of International Conference on Communication and Networks: ComNet 2016 (19-10 February). Springer Singapore. https://doi.org/10.1007/978-981-10-2750-5_32
  42. Marcu I, Suciu G, Bălăceanu C, Vulpe A & Drăgulinescu A M (2020). Arrowhead technology for digitalization and automation solution: Smart cities and smart agriculture. Sensors, 20(5): 1464. https://doi.org/10.3390/s20051464
  43. Marimuthu M, D'Souza C & Shukla Y (2022). Integrating community value into the adoption framework: A systematic review of conceptual research on participatory smart city applications. Technological Forecasting and Social Change 181: 121779. https://doi.org/10.1016/j.techfore.2022.121779
  44. devices, Mekala M S & Viswanathan P (2017). A Survey: Smart agriculture IoT with cloud computing, 1-7. In 2017 international conference on microelectronic circuits and systems (ICMDCS) (10-12 August 2017, Tamil Nadu). IEEE. https://doi.org/10.1109/ICMDCS.2017.8211551
  45. Mishbah M, Purwandari B & Sensuse D I (2018). Systematic review and meta-analysis of proposed smart village conceptual model: Objectives, strategies, dimensions, and foundations, pp. 127-133. In 2018
  46. International Conference on Information Technology Systems and Innovation (ICITSI) (22-26 October, Bandung- Padang) IEEE. https://doi.org/10.1109/ICITSI.2018.8696029
  47. Morkunas M & Volkov A (2023). The progress of the development of a climate-smart agriculture in Europe: Is there cohesion in the European Union? Environmental Management 71(6): 1111-1127. https://doi.org/10.1007/s00267-022-01782-w
  48. Muangprathub J, Boonnam N, Kajornkasirat S, Lekbangpong N, Wanichsombat A & Nillaor P (2019). IoT and agriculture data analysis for smart farm. Computers and electronics in agriculture 156: 467-474. https://doi.org/10.1016/j.compag.2018.12.011
  49. Muhammad K B, Soomro T R, Butt J, Saleem H, Khan M A & Saleem S (2022). IoT and cloud based smart agriculture framework to improve crop yield meeting world's food needs. International Journal of Computer Science and Network Security 22(2): 7. https://doi.org/10.22937/IJCSNS.2022.22.6.52
  50. Muhsen Y R & Al-hchaimi A A J (2024). Modelling intelligent agriculture decision support tools to boost sustainable digitalization: Evidence from MCDM methods, pp. 93-105. In International Conference on Explainable Artificial Intelligence in the Digital Sustainability (19 June, Basrah). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-63717-9_6 Mutenje M J, Farnworth C R, Stirling C, Thierfelder C, Mupangwa W & Nyagumbo I (2019). A cost-benefit analysis of climate-smart agriculture options in Southern Africa: Balancing gender and technology. Ecological Economics 163: 126-137. https://doi.org/10.1016/j.ecolecon.2019.05.013
  51. Muzari W, Gatsi W & Muvhunzi S (2012). The impacts of technology adoption on smallholder agricultural productivity in sub-Saharan Africa: A review. Journal of Sustainable Development 5(8): 69. http://dx.doi.org/10.5539/jsd.v5n8p69
  52. Ntaliani M, Costopoulou C, Karetsos S, Tambouris E & Tarabanis K (2010). Agricultural e-government services: An implementation framework and case study. Computers and electronics in agriculture 70(2): 337-347. https://doi.org/10.1016/j.compag.2009.09.008
  53. Öztaş R G (2019). Integration of ICT Supported Innovative Solutions to Rural Area In Planning: The Case of Vodafone Smart Village, Gazi University (Unpublished) Master Thesis, Ankara, 192 pp (In Turkish)
  54. Öztaş Karlı R G (2020). From smart cities to smart villages, 30-47. Theory and Research in Architecture, Planning, and Design, (Eds. R. Kasmo & L. Kudumovic), Gece kitaplığı. (In Turkish)
  55. Öztaş Karlı R G, Karlı H & Çelikyay H S (2022). Investigating the acceptance of shared e-scooters: Empirical evidence from Turkey. Case Studies on Transport Policy 10(2): 1058-1068. https://doi.org/10.1016/j.cstp.2022.03.018
  56. Öztaş Karlı RG, Özüduru B & Çelikyay S (2023). ICT-supported applications in rural area planning: Vodafone Smart Village model. Journal of Agriculture Faculty of Ege University 60(4): 541-559. https://doi.org/10.20289/zfdergi.1273336
  57. Philip L & Williams F (2019). Healthy ageing in smart villages? Observations from the field. European Countryside, 11(4): 616-633. https://doi.org/10.2478/euco-2019-0034
  58. Prokopy L S, Arbuckle J G, Barnes A P, Haden V R, Hogan A, Niles M T & Tyndall J (2015). Farmers and climate change: A cross-national comparison of beliefs and risk perceptions in high-income countries. Environmental Management 56: 492-504. https://doi.org/10.1007/s00267-015-0504-2
  59. Raji E, Ijomah T I & Eyieyien O G (2024). Integrating technology, market strategies, and strategic management in agricultural economics for enhanced productivity. International Journal of Management & Entrepreneurship Research 6(7): 2112-2124. https://doi.org/10.51594/ijmer.v6i7.1260
  60. Rajkumar S, Ramachandran M, Saravanan V & Nanjundan P (2023). Evaluation of a WSM system for a smart system in agricultural systems. Building Materials and Engineering Structures 1(2): 17-25. http://doi.org/10.46632/bmes/1/2/3
  61. Rehman K U, Andleeb S, Ashfaq M, Akram N & Akram M W (2023). Blockchain-enabled smart agriculture: Enhancing data-driven decision management making and ensuring food security. Journal of Cleaner Production 427: 138900. https://doi.org/10.1016/j.jclepro.2023.138900
  62. Schwering D S, Bergmann L & Sonntag W I (2022). How to encourage farmers to digitize? A study on user typologies and motivations of farm information systems. Computers and Electronics in Agriculture 199: 107133. https://doi.org/10.1016/j.compag.2022.107133
  63. Sennuga S O & Oyewole S O (2020). Exploring the effectiveness of agricultural technologies training among smallholder farmers in Sub Saharan African communities. European Journal of Training and Development Studies 7(4): 1-15
  64. Shahsavar S, Jafari Rad A, Afzal P, Nezafati N & Akhavan Aghdam M (2019). Prospecting for polymetallic mineralization using step-wise weight assessment ratio analysis (SWARA) and fractal modeling in Aghkand Area, NW Iran. Arabian Journal of Geosciences 12: 1-10. https://doi.org/10.1007/s12517-019-4304-5
  65. Singh G & Kaur G (2021). Digital technologies for smart agriculture, 54-67. In: Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture, (Eds. P. Tomar & G. Kaur), IGI Global, 400 pp Sizirici B, Fseha Y, Cho C S, Yildiz I & Byon Y J (2021). A review of carbon footprint reduction in construction industry, from design to operation. Materials 14(20): 6094. https://doi.org/10.3390/ma14206094
  66. Somwanshi R, Shindepatil U, Tule D, Mankar A, Ingle N, Rajamanya G B D V & Deshmukh A (2016). Study and development of village as a smart village. International Journal of Scientific & Engineering Research 7(6): 395-408
  67. Soni K, Waghela D, Shah R & Mohan M (2018). Smart well monitoring system, 1-5. In 2018 International Conference on Smart City and Emerging Technology (ICSCET) (5 January, 2018). IEEE. https://doi.org/10.1109/ICSCET.2018.8537264
  68. Stoian M, Dobre I, Popescu C G, Vasile M C, Dimitriu A T & Ion A (2022). Increasing sustainability of food production and ensuring human health through agriculture digitalization. Economics of Agriculture 69(4): 1209-1223. https://doi.org/10.5937/ekoPolj2204209S
  69. SVI (2019). Smart villages. (Web page: https://smartvillage.ieee.org/about-ieee-smart-village/) (Data accessed: July 2024). Tey Y S & Brindal M (2012). Factors influencing the adoption of precision agricultural technologies: A review for policy implications. Precision Agriculture 13: 713-730. https://doi.org/10.1007/s11119-012-9273-6
  70. United Nations (2024). THE 17 GOALS. (Web page: https://sdgs.un.org/goals) (Data accessed: July 2024). Utlu N (2024). Digital marketing strategy selection with SWARA method. Doğuş Üniversitesi Dergisi 25(1): 341-355. https://doi.org/10.31671/doujournal.1356008 (In Turkish)
  71. Uztürk D & Büyüközkan G (2022). Smart agriculture technology evaluation: A linguistic-based MCDM methodology, 161-174. Proceedings of the 5th Symposium on Agri-Tech Economics for Sustainable Futures (19-20 September, 2022, Newport). https://doi.org/10.22004/ag.econ.337128
  72. Wang H, Chung J E, Park N, McLaughlin M L & Fulk J (2012). Understanding online community participation: A technology acceptance perspective. Communication Research 39(6): 781-801. https://doi.org/10.1177/0093650211408593
  73. Watson J K (2019). Energy diversification and self-sustainable smart villages, 99-109. In: Smart Villages in the EU and Beyond (Eds. A. Visvizi, M.D. Lytras & G. Mudri). Emerald Publishing Limited 208 pp. https://doi.org/10.1108/978-1-78769-845-120191008
  74. Yang C, Ji X, Cheng C, Liao S, Obuobi B & Zhang Y (2024). Digital economy empowers sustainable agriculture: Implications for farmers’ adoption of ecological agricultural technologies. Ecological Indicators 159: 111723. https://doi.org/10.1016/j.ecolind.2024.111723R
  75. Yigezu Y A, Mugera A, El-Shater T, Aw-Hassan A, Piggin C, Haddad A. & Loss S (2018). Enhancing adoption of agricultural technologies requiring high initial investment among smallholders. Technological Forecasting and Social Change 134: 199-206. https://doi.org/10.1016/j.techfore.2018.06.006
  76. Zavratnik V, Kos A & Stojmenova Duh E (2018). Smart villages: Comprehensive review of initiatives and practices. Sustainability 10(7): 2559. https://doi.org/10.3390/su10072559
  77. Zhang X & Zhang Z (2020). How do smart villages become a way to achieve sustainable development in rural areas? Smart village planning and practices in China. Sustainability 12(24) 10510. https://doi.org/10.3390/su122410510
  78. Zolfani S H & Banihashemi S S A (2014). Personnel selection based on a novel model of game theory and MCDM approaches, 15-16. In Proc. of 8th International Scientific Conference Business and Management (15-16 May 2014, Vilnius). http://dx.doi.org/10.3846/bm.2014.024
  79. Zolfani S H & Saparauskas J (2013). New application of SWARA method in prioritizing sustainability assessment indicators of energy system. Engineering Economics 24(5): 408-414. https://doi.org/10.5755/j01.ee.24.5.4526
  80. Anderson A, Loomba P, Orajaka I, Numfor J, Saha S, Janko S. & Larsen R (2017). Empowering smart communities: electrification, education, and sustainable entrepreneurship in IEEE Smart Village Initiatives. IEEE Electrification Magazine 5(2): 6-16. https://doi.org/10.1109/MELE.2017.2685738
  81. Philip L & Williams F (2019). Healthy ageing in smart villages? Observations from the field. European Countryside, 11(4): 616-633. https://doi.org/10.2478/euco-2019-0034
  82. Yigezu Y A, Mugera A, El-Shater T, Aw-Hassan A, Piggin C, Haddad A. & Loss S (2018). Enhancing adoption of agricultural technologies requiring high initial investment among smallholders. Technological Forecasting and Social Change 134: 199-206. https://doi.org/10.1016/j.techfore.2018.06.006