- [1] Zhang, H., An, R., & Zhong, Q. (2019). Anti-corruption, government subsidies, and investment efficiency. China Journal of Accounting Research, 12(1), 113-133.
- [2] Singh, B., Sharma, K. P., Sharma, N., & Kumar, P. (2021). Blockchain-based Claim Verification and Approval System for Disbursing Fertilizer Subsidy. Available at SSRN 3814155.
- [3] Adewuyi, A. (2020). Challenges and prospects of renewable energy in Nigeria: A case of bioethanol and biodiesel production. Energy Reports, 6, 77-88.
- [4] Bala, K., & Kaur, P. D. (2022). Transparent subsidized agri‐product distribution during pandemics with reputation based PoA blockchain. Concurrency and Computation: Practice and Experience, e6863.
- [5] Farajzadeh, Z., & Bakhshoodeh, M. (2015). Economic and environmental analyses of Iranian energy subsidy reform using Computable General Equilibrium (CGE) model. Energy for Sustainable Development, 27, 147-154.
- [6] Barkhordar, Z. A., Fakouriyan, S., & Sheykhha, S. (2018). The role of energy subsidy reform in energy efficiency enhancement: Lessons learnt and future potential for Iranian industries. Journal of Cleaner Production, 197, 542-550.
- [7] Khalilian, S., & Yuzbashkandi, S. S. (2021). Analysis of vegetable oil demand and its price reform in Iran: using rural and urban household level data. International Journal of Agriculture Environment and Food Sciences, 5(1), 122-132.
- [8] Mosavi, S. H. (2016). Energy price reform and food markets: The case of bread supply chain in Iran. Agricultural Economics, 47(2), 169-179.
- [9] Baharmand, H., Maghsoudi, A., & Coppi, G. (2021). Exploring the application of blockchain to humanitarian supply chains: insights from Humanitarian Supply Blockchain pilot project. International Journal of Operations & Production Management.
- [10] Wang, B., Luo, W., Zhang, A., Tian, Z., & Li, Z. (2020). Blockchain-enabled circular supply chain management: A system architecture for fast fashion. Computers in Industry, 123, 103324.
- [11] Cole, R., Stevenson, M. and Aitken, J. (2019), Blockchain technology: implications for operations and supply chain management, Supply Chain Management, 24 (4), 469-483.
- [12] Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831.
- [13] Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, 101967.
- [14] Irannezhad, M., Shokouhyar, S., Ahmadi, S., & Papageorgiou, E. I. (2021). An integrated FCM-FBWM approach to assess and manage the readiness for blockchain incorporation in the supply chain. Applied Soft Computing, 112, 107832.
- [15] Sternberg, H. S., Hofmann, E., & Roeck, D. (2021). The struggle is real: insights from a supply chain blockchain case. Journal of Business Logistics, 42(1), 71-87.
- [16] Nguyen, S., Chen, P. S. L., & Du, Y. (2020). Risk identification and modeling for blockchain-enabled container shipping. International Journal of Physical Distribution & Logistics Management.
- [17] Kshetri, N. (2021). Blockchain and sustainable supply chain management in developing countries. International Journal of Information Management, 60, 102376.
- [18] Farooque, M., Jain, V., Zhang, A., & Li, Z. (2020). Fuzzy DEMATEL analysis of barriers to Blockchain-based life cycle assessment in China. Computers & Industrial Engineering, 147, 106684.
- [19] Ali, M. H., Chung, L., Kumar, A., Zailani, S., & Tan, K. H. (2021). A sustainable Blockchain framework for the halal food supply chain: Lessons from Malaysia. Technological Forecasting and Social Change, 170, 120870.
- [20] Budak, A., & Çoban, V. (2021). Evaluation of the impact of blockchain technology on supply chain using cognitive maps. Expert Systems with Applications, 184, 115455.
- [21] Bamakan, S. M. H., Moghaddam, S. G., & Manshadi, S. D. (2021). Blockchain-enabled pharmaceutical cold chain: applications, key challenges, and future trends. Journal of Cleaner Production, 302, 127021.
- [22] Özkan, B., Kaya, İ., Erdoğan, M., & Karaşan, A. (2019, July). Evaluating blockchain risks by using a MCDM methodology based on pythagorean fuzzy sets. In International conference on intelligent and fuzzy systems (pp. 935-943). Springer, Cham.
- [23] Prewett, K. W., Prescott, G. , & Phillips, K. (2020). Blockchain adoption is inevitable—Barriers and risks remain. Journal of Corporate accounting & finance, 31(2), 21-28.
- [24] Vafadarnikjoo, A., Badri Ahmadi, H., Liou, J. J., Botelho, T., & Chalvatzis, K. (2021). Analyzing blockchain adoption barriers in manufacturing supply chains by the neutrosophic analytic hierarchy process. Annals of Operations Research, 1-28.
- [25] Mathivathanan, , Mathiyazhagan, K., Rana, N. P., Khorana, S., & Dwivedi, Y. K. (2021). Barriers to the adoption of blockchain technology in business supply chains: a total interpretive structural modelling (TISM) approach. International Journal of Production Research, 59(11), 3338-3359.
- [26] Etemadi, N., Van Gelder, P., & Strozzi, F. (2021). An ism modeling of barriers for blockchain/distributed ledger technology adoption in supply chains towards cybersecurity. Sustainability, 13(9), 4672.
- [27] Sahebi, I. G., Masoomi, B., & Ghorbani, S. (2020). Expert oriented approach for analyzing the blockchain adoption barriers in humanitarian supply chain. Technology in Society, 63, 101427.
- [28] Baharmand, H., Saeed, N., Comes, T., & Lauras, M. (2021). Developing a framework for designing humanitarian blockchain projects. Computers in Industry, 131, 103487.
- [29] Friedman, N., & Ormiston, J. (2022). Blockchain as a sustainability-oriented innovation?: Opportunities for and resistance to Blockchain technology as a driver of sustainability in global food supply chains. Technological Forecasting and Social Change, 175, 121403.
- [30] Biswas, B., & Gupta, R. (2019). Analysis of barriers to implement blockchain in industry and service sectors. Computers & Industrial Engineering, 136, 225-241.
- [31] Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
- [32] Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?. Omega, 87, 205-225.
- [33] Michnik, J., & Adamus-Matuszyńska, A. (2015). Structural analysis of problems in public relations. Multiple Criteria Decision Making, (10), 105-123.
- [34] Banaś, D., & Michnik, J. (2019). Evaluation of the Impact of Strategic Offers on the Financial and Strategic Health of the Company—A Soft System Dynamics Approach. Mathematics, 7(2), 208.
- [35] Michnik, J., & Grabowski, A. (2020). Modeling Uncertainty in the Wings Method Using Interval Arithmetic. International Journal of Information Technology & Decision Making, 19(01), 221–240.
- [36] Kaviani, M.A., Tavana, M., Kumar, A., Michnik, J., Niknam, R. and Campos, E.A.R. (2020). An Integrated Framework for Evaluating the Barriers to Successful Implementation of Reverse Logistics in the Automotive Industry. Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2020.122714.
- [37] Wang, W., Tian, , Xi, W., Tan, Y. R., & Deng, Y. (2020). The influencing factors of China’s green building development: An analysis using RBF-WINGS method. Building and Environment, 107425.
- [38] Tavana, M., Mousavi, H., Nasr, A. K., & Mina, H. (2021a). A Fuzzy Weighted Influence Non-linear Gauge System with Application to Advanced Technology Assessment at NASA. Expert Systems with Applications, In press.
- [39] Michnik, J. (2013). Weighted Influence Non-linear Gauge System (WINGS)–An analysis method for the systems of interrelated components. European Journal of Operational Research, 228(3), 536-544.
- [40] Tavana, M., Nasr, A. K., Mina, H., & Michnik, J. (2021b). A private sustainable partner selection model for green public-private partnerships and regional economic development. Socio-Economic Planning Sciences, 101189.
- [41] Bozorgi-Amiri, A., Ranjbar, A., & Jamali, A. (2019). A Novel Hybrid MCDM Method for Optimal Location Selection of Free Trade Zones, Case Study: Mazandaran Province. Advances in Industrial Engineering, 53(3), 79-92.
- [42] Barzinpour, F., & Karimi, S. (2014). Forecasting Effects of Scenarios of Subsides Removal on Residential Electricity Consumption by Artificial Neural Networks. Advances in Industrial Engineering, 48(Special Issue), 83-90.
|