Investigating the Internet-of-Things (IoT) risks for supply chain management using q-rung orthopair fuzzy-SWARA-ARAS framework
Abstract
Modern “Supply Chains (SCs)” have recently been introduced as value networks of high complexity, and firms have focused on its efficiency as an important support for staying competitive in the market. Firms are currently capable of observing, tracking, and monitoring their products, activities, and processes throughout their value chain networks using new technologies, namely the “Internet of Things (IoT)”. Though, the influencing factors of IoT are highly complex and diverse, which result in the information-intensiveness of the SCs processes. This, in turn, leads to lots of barriers to SCs. In this paper, we evaluate and rank the IoT risks for “Supply Chain Management (SCM)” by utilizing “Stepwise Weight Assessment Ratio Analysis (SWARA)” and “Additive Ratio Assessment (ARAS)” under “q-Rung Orthopair Fuzzy Sets (q-ROFSs)”. A case study is presented for investigating the IoT risks for SCM in the q-ROFSs setting. Moreover, the obtained results were compared to those of some methods currently used in the literature. The outcomes of the study show that the security and privacy risks with a weight value of 0.0572 is the main IoT risk factor for the SCM and the organization-I with the utility degree 0.8208 is the best option with diverse IoT risks for SCM.
First published online 25 April 2022
Keyword : Internet of Things (IoT), supply chain, q-rung orthopair fuzzy sets, SWARA, multi-criteria decision-making, ARAS
How to Cite
Hu, Y., Al-Barakati, A., & Rani, P. (2022). Investigating the Internet-of-Things (IoT) risks for supply chain management using q-rung orthopair fuzzy-SWARA-ARAS framework. Technological and Economic Development of Economy, 1-26. https://doi.org/10.3846/tede.2022.16583
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ahmed, E., Yaqoob, I., Hashem, I. A.T., Khan, I., Ahmed, A. I. A., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in Internet of Things. Computer Networks, 129, 459–471. https://doi.org/10.1016/j.comnet.2017.06.013
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Badia-Melis, R., Mc Carthy, U., Ruiz-Garcia, L., Garcia-Hierro, J., & Robla Villalba, J. I. (2018). New trends in cold chain monitoring applications – A review. Food Control, 86, 170–182. https://doi.org/10.1016/j.foodcont.2017.11.022
Bardaki, C., Kourouthanassis, P., & Pramatari, K. (2012). Deploying RFID-enabled services in the retail supply chain: Lessons learned toward the Internet of Things. Information Systems Management, 29(3), 233–245. https://doi.org/10.1080/10580530.2012.687317
Bauk, S., Drbakovi, M., & Schmeink, A. J. P. (2017). Challenges of tagging goods in supply chains and a cloud perspective with focus on some transitional economies. Transportation, 29(1), 109–120. https://doi.org/10.7307/ptt.v29i1.2162
Belinski, R., Peixe, A. M. M., Frederico, G. F., & Garza-Reyes, J. A. (2020). Organizational learning and Industry 4.0: Findings from a systematic literature review and research agenda. Benchmarking: An International Journal, 27(8), 2435–2457. https://doi.org/10.1108/BIJ-04-2020-0158
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742. https://doi.org/10.1080/00207543.2017.1402140
Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39–61. https://doi.org/10.1108/SCM-03-2018-0142
Bogle, I. D. L. (2017). A Perspective on smart process manufacturing research challenges for process systems engineers. Engineering, 3(2), 161–165. https://doi.org/10.1016/J.ENG.2017.02.003
Boos, D., Guenter, H., Grote, G., & Kinder, K. (2013). Controllable accountabilities: The Internet of Things and its challenges for organisations. Behaviour & Information Technology, 32(5), 449–467. https://doi.org/10.1080/0144929X.2012.674157
Büyüközkan, G., & Güler, M. (2020). Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique. Measurement, 153, 107353. https://doi.org/10.1016/j.measurement.2019.107353
Cavalcante, E., Pereira, J., Alves, M. P., Maia, P., Moura, R., Batista, T., Delicato, F. C., & Pires, P. F. (2016a). On the interplay of Internet of Things and cloud computing: A systematic mapping study. Computer Communications, 89–90, 17–33. https://doi.org/10.1016/j.comcom.2016.03.012
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016b). Computational intelligence and financial markets: A survey and future directions. Expert Systems With Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Cavallaro, F. (2010). Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Applied Energy, 87(2), 496–503. https://doi.org/10.1016/j.apenergy.2009.07.009
Cheng, S., Jianfu, S., Alrasheedi, M., Saeidi, P., Mishra, A. R., & Rani, P. (2021). A New extended VIKOR approach using q-rung orthopair fuzzy sets for sustainable enterprise risk management assessment in manufacturing small and medium-sized enterprises. International Journal of Fuzzy Systems, 23, 1347–1369. https://doi.org/10.1007/s40815-020-01024-3
De Cremer, D., Nguyen, B., & Simkin, L. (2017). The integrity challenge of the Internet-of-Things (IoT): On understanding its dark side. Journal of Marketing Management, 33(1–2), 145–158. https://doi.org/10.1080/0267257X.2016.1247517
Dehnavi, A., Aghdam, I. N., Pradhan, B., & Morshed Varzandeh, M. H. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA, 135, 122–148. https://doi.org/10.1016/j.catena.2015.07.020
Díaz, M., Martín, C., & Rubio, B. (2016). State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. Journal of Network and Computer Applications, 67, 99–117. https://doi.org/10.1016/j.jnca.2016.01.010
Docherty, I., Marsden, G., & Anable, J. (2018). The governance of smart mobility. Transportation Research Part A: Policy and Practice, 115, 114–125. https://doi.org/10.1016/j.tra.2017.09.012
Dutton, W. H. (2014). Putting things to work: social and policy challenges for the Internet of things. info, 16(3), 1–21. https://doi.org/10.1108/info-09-2013-0047
Dweekat, A. J., Hwang, G., & Park, J. (2017). A supply chain performance measurement approach using the internet of things. Industrial Management & Data Systems, 117(2), 267–286. https://doi.org/10.1108/IMDS-03-2016-0096
Eling, M., & Schnell, W. (2016). What do we know about cyber risk and cyber risk insurance? The Journal of Risk Finance, 17(5), 474–491. https://doi.org/10.1108/JRF-09-2016-0122
Eurich, M., Oertel, N., & Boutellier, R. (2010). The impact of perceived privacy risks on organizations’ willingness to share item-level event data across the supply chain. Electronic Commerce Research, 10, 423–440. https://doi.org/10.1007/s10660-010-9062-0
Fan, H., Li, G., Sun, H., & Cheng, T. C. E. (2017). An information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences. International Journal of Production Economics, 185, 63–75. https://doi.org/10.1016/j.ijpe.2016.11.015
Friedewald, M., & Raabe, O. (2011). Ubiquitous computing: An overview of technology impacts. Telematics and Informatics, 28(2), 55–65. https://doi.org/10.1016/j.tele.2010.09.001
Ghanbari, A., Laya, A., Alonso-Zarate, J., & Markendahl, J. (2017). Business development in the Internet of Things: A matter of vertical cooperation. IEEE Communications Magazine, 55(2), 135–141. https://doi.org/10.1109/MCOM.2017.1600596CM
Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32–47. https://doi.org/10.1016/j.comcom.2014.07.013
Gu, F., Ma, B., Guo, J., Summers, P. A., & Hall, P. (2017). Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study. Waste Management, 68, 434–448. https://doi.org/10.1016/j.wasman.2017.07.037
Gu, Y., & Liu, Q. (2013). Research on the application of the internet of things in reverse logistics information management. Journal of Industrial Engineering and Management, 6(4), 963–973. https://doi.org/10.3926/jiem.793
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
Guo, T., Yu, K., Srivastava, G., Wei, W., Guo, L., & Xiong, N. N. (2021). Latent discriminative low-rank projection for visual dimension reduction in green Internet of Things. IEEE Transactions on Green Communications and Networking, 5(2), 737–749. https://doi.org/10.1109/TGCN.2021.3062972
Harris, I., Wang, Y., & Wang, H. (2015). ICT in multimodal transport and technological trends: Unleashing potential for the future. International Journal of Production Economics, 159, 88–103. https://doi.org/10.1016/j.ijpe.2014.09.005
He, J., Huang, Z., Mishra, A. R., & Alrasheedi, M. (2021). Developing a new framework for conceptualizing the emerging sustainable community-based tourism using an extended interval-valued Pythagorean fuzzy SWARA-MULTIMOORA. Technological Forecasting and Social Change, 171, 120955. https://doi.org/10.1016/j.techfore.2021.120955
He, L., Xue, M., & Gu, B. (2020). Internet-of-things enabled supply chain planning and coordination with big data services: Certain theoretic implications. Journal of Management Science and Engineering, 5(1), 1–22. https://doi.org/10.1016/j.jmse.2020.03.002
Ho-Sam-Sooi, N., Pieters, W., & Kroesen, M. (2021). Investigating the effect of security and privacy on IoT device purchase behaviour. Computers & Security, 102, 102132. https://doi.org/10.1016/j.cose.2020.102132
Iordache, M., Schitea, D., Deveci, M., Akyurt, İ. Z., & Iordache, I. (2019). An integrated ARAS and interval type-2 hesitant fuzzy sets method for underground site selection: Seasonal hydrogen storage in salt caverns. Journal of Petroleum Science and Engineering, 175, 1088–1098. https://doi.org/10.1016/j.petrol.2019.01.051
Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the Internet of Things: Perspectives and challenges. Wireless Networks, 20, 2481–2501. https://doi.org/10.1007/s11276-014-0761-7
Karabasevic, D., Paunkovic, J., & Stanujkic, D. (2016a). Ranking of companies according to the indicators of corporate social responsibility based on SWARA and ARAS methods. Serbian Journal of Management, 11(1), 43–53. https://doi.org/10.5937/sjm11-7877
Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016b). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27(1), 49–65. https://doi.org/10.15388/Informatica.2016.76
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57–81. https://doi.org/10.1016/j.jnca.2016.08.002
Keršulienė, 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
Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022
Krishankumar, R., Ravichandran, K. S., Kar, S., Cavallaro, F., Zavadskas, E. K., & Mardani, A. (2019). Scientific decision framework for evaluation of renewable energy sources under q-rung orthopair fuzzy set with partially known weight information. Sustainability, 11(15), 4202. https://doi.org/10.3390/su11154202
Krishankumar, R., Nimmagadda, A. S., Rani, P., Mishra, A. R., Ravichandran, K. S., & Gandomi, A. H. (2021). Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach. Journal of Cleaner Production, 279, 123329. https://doi.org/10.1016/j.jclepro.2020.123329
Kshetri, N. (2017). Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, 41(10), 1027–1038. https://doi.org/10.1016/j.telpol.2017.09.003
Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440. https://doi.org/10.1016/j.bushor.2015.03.008
Leone, L. (2017). Beyond connectivity: The Internet of Food architecture between ethics and the EU citizenry. Journal of Agricultural and Environmental Ethics, 30, 423–438. https://doi.org/10.1007/s10806-017-9675-6
Li, J., Feng, G., Wei, W., Luo, C., Cheng, L., Wang, H., Song, H., & Ming, Z. (2018). PSOTrack: A RFID-based system for random moving objects tracking in unconstrained indoor environment. IEEE Internet of Things Journal, 5(6), 4632–4641. https://doi.org/10.1109/JIOT.2018.2795893
Li, J., Maiti, A., Springer, M., & Gray, T. (2020). Blockchain for supply chain quality management: Challenges and opportunities in context of open manufacturing and industrial internet of things. International Journal of Computer Integrated Manufacturing, 33(12), 1321–1355. https://doi.org/10.1080/0951192X.2020.1815853
Li, J., Zhang, G., Wei, W., Wang, Z., & Zhang, J. (2013). Analysis of wireless link characteristics in RFID location-network. Information Technology Journal, 12(11), 2207–2212. https://doi.org/10.3923/itj.2013.2207.2212
Li, Q., Luo, H., Xie, P.-X., Feng, X.-Q., & Du, R.-Y. (2015). Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment. Computers in Industry, 70, 23–45. https://doi.org/10.1016/j.compind.2015.01.011
Liu, F., Tan, C.-W., Lim, E. T. K., & Choi, B. (2017). Traversing knowledge networks: An algorithmic historiography of extant literature on the Internet of Things (IoT). Journal of Management Analytics, 4(1), 3–34. https://doi.org/10.1080/23270012.2016.1214540
Liu, P., & Liu, W. Q. (2019). Multiple-attribute group decision-making based on power Bonferroni operators of linguistic q-rung orthopair fuzzy numbers. International Journal of Intelligent Systems, 34(4), 652–689. https://doi.org/10.1002/int.22071
Liu, P. Liu, P., Wang, P., & Zhu, B. (2019). An extended multiple attribute group decision making method based on q-rung orthopair fuzzy numbers. IEEE Access, 7, 162050–162061. https://doi.org/10.1109/ACCESS.2019.2951357
Liu, P., & Wang, P. (2018). Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems, 33(2), 259–280. https://doi.org/10.1002/int.21927
Lowry, P. B., Dinev, T., & Willison, R. (2017). Why security and privacy research lies at the centre of the information systems (IS) artefact: Proposing a bold research agenda. European Journal of Information Systems, 26(6), 546–563. https://doi.org/10.1057/s41303-017-0066-x
Mathaba, S., Adigun, M., Oladosu, J., & Oki, O. (2017). On the use of the Internet of Things and Web 2.0 in inventory management. Journal of Intelligent & Fuzzy Systems, 32(4), 3091–3101. https://doi.org/10.3233/JIFS-169252
Meng, L. (2021). Using IoT in supply chain risk management, to enable collaboration between business, community, and government. Smart Cities, 4(3), 995–1003. https://doi.org/10.3390/smartcities4030052
Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516. https://doi.org/10.1016/j.adhoc.2012.02.016
Mishra, A., Sisodia, G., Raj Pardasani, K., & Sharma, K. (2020a). Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology. Iranian Journal of Fuzzy Systems, 17, 55–68.
Mishra, A. R., Rani, P., Pandey, K., Mardani, A., Streimikis, J., Streimikiene, D., & Alrasheedi, M. (2020b). Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability, 12(10), 4155. https://doi.org/10.3390/su12104155
Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., & Kar, S. (2021). An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19). Applied Soft Computing, 103, 107155. https://doi.org/10.1016/j.asoc.2021.107155
Musa, A., & Dabo, A. A. A. (2016). A review of RFID in supply chain management: 2000–2015. Global Journal of Flexible Systems Management, 17, 189–228. https://doi.org/10.1007/s40171-016-0136-2
Neirotti, P., Raguseo, E., & Paolucci, E. (2018). How SMEs develop ICT-based capabilities in response to their environment. Journal of Enterprise Information Management, 31(1), 10–37. https://doi.org/10.1108/JEIM-09-2016-0158
Ochoa, S. F., Fortino, G., & Di Fatta, G. (2017). Cyber-physical systems, internet of things and big data. Future Generation Computer Systems, 75, 82–84. https://doi.org/10.1016/j.future.2017.05.040
Pamucar, D., & Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method- FUCOM-F. Facta Universitatis, Series: Mechanical Engineering, 18(3), 419–437. https://doi.org/10.22190/FUME200602034P
Pamucar, D., Stevic, Z., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
Parry, G. C., Brax, S. A., Maull, R. S., & Ng, I. C. L. (2016). Operationalising IoT for reverse supply: The development of use-visibility measures. Supply Chain Management: An International Journal, 21(2), 228–244. https://doi.org/10.1108/SCM-10-2015-0386
Peng, X. (2019). Algorithm for Pythagorean fuzzy multi-criteria decision making based on WDBA with new score function. Fundamenta Informaticae, 165(2), 99–137. https://doi.org/10.3233/FI-2019-1778
Peng, X., & Liu, L. (2019). Information measures for q-rung orthopair fuzzy sets. International Journal of Intelligent Systems, 34(8), 1795–1834. https://doi.org/10.1002/int.22115
Qi, S., Lu, Y., Wei, W., & Chen, X. (2021). Efficient data access control with fine-grained data protection in cloud-assisted IIoT. IEEE Internet of Things Journal, 8(4), 2886–2899. https://doi.org/10.1109/JIOT.2020.3020979
Qiu, X., Luo, H., Xu, G., Zhong, R., & Huang, G. Q. (2015). Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). International Journal of Production Economics, 159, 4–15. https://doi.org/10.1016/j.ijpe.2014.09.001
Rani, P., & Mishra, A. R. (2020). Multi-criteria weighted aggregated sum product assessment framework for fuel technology selection using q-rung orthopair fuzzy sets. Sustainable Production and Consumption, 24, 90–104. https://doi.org/10.1016/j.spc.2020.06.015
Rani, P., Mishra, A. R., Pardasani, K. R., Mardani, A., Liao, H., & Streimikiene, D. (2019) A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production, 238, 117936. https://doi.org/10.1016/j.jclepro.2019.117936
Rani, P., Mishra, A. R., Saha, A., & Pamucar, D. (2021). Pythagorean fuzzy weighted discrimination‐based approximation approach to the assessment of sustainable bioenergy technologies for agricultural residues. International Journal of Intelligent Systems, 36(6), 2964–2990. https://doi.org/10.1002/int.22408
Ren, J., Guo, H., Xu, C., & Zhang, Y. (2017). Serving at the edge: A scalable IoT architecture based on transparent computing. IEEE Network, 31(5), 96–105. https://doi.org/10.1109/MNET.2017.1700030
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rogers, P. R., Miller, A., & Judge, W. Q. (1999). Using information-processing theory to understand planning/performance relationships in the context of strategy. Strategic Management Journal, 20(6), 567–577. https://doi.org/10.1002/(SICI)1097-0266(199906)20:6<567::AID-SMJ36>3.0.CO;2-K
Rymaszewska, A., Helo, P., & Gunasekaran, A. (2017). IoT powered servitization of manufacturing – an exploratory case study. International Journal of Production Economics, 192, 92–105. https://doi.org/10.1016/j.ijpe.2017.02.016
Saaty, T. L. (1980). The analytical hierarchy process. McGraw-Hill.
Saaty, T. L. (1999, August). Fundamentals of analytic network process. In Proceedings of the International Symposium on the Analytic Hierarchy Process (pp. 348–379). Japan, Kobe. https://doi.org/10.13033/isahp.y1999.038
Stanujkic, D. (2015). Extension of the ARAS method for decision-making problems with interval-valued triangular fuzzy numbers. Informatica, 26(2), 335–355. https://doi.org/10.15388/Informatica.2015.51
Strange, R., & Zucchella, A. (2017). Industry 4.0, global value chains and international business. Multinational Business Review, 25(3), 174–184. https://doi.org/10.1108/MBR-05-2017-0028
Strous, L., von Solms, S., & Zúquete, A. (2021). Security and privacy of the Internet of Things. Computers & Security, 102, 102148. https://doi.org/10.1016/j.cose.2020.102148
Tang, G., Chiclana, F., & Liu, P. (2020). A decision-theoretic rough set model with q-rung orthopair fuzzy information and its application in stock investment evaluation. Applied Soft Computing, 91, 106212. https://doi.org/10.1016/j.asoc.2020.106212
Thomas, R. (2014). In modern supply chains, the soft stuff is the hard stuff. International Journal of Physical Distribution & Logistics Management, 44(6), 1–10. https://doi.org/10.1108/IJPDLM-05-2014-0100
Tu, M. (2018). An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management. The International Journal of Logistics Management, 29(1), 131–151. https://doi.org/10.1108/IJLM-11-2016-0274
Turskis, Z., & Zavadskas, E. K. (2010). A novel method for multiple criteria analysis: Grey additive ratio assessment (ARAS-G) method. Informatica, 21(4), 597–610. https://doi.org/10.15388/Informatica.2010.307
Wang, J., Wei, W., Wang, W., & Li, R. (2018). RFID hybrid positioning method of phased array antenna based on neural network. IEEE Access, 6, 74953–74960. https://doi.org/10.1109/ACCESS.2018.2877396
Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The Internet of Things – A survey of topics and trends. Information Systems Frontiers, 17, 261–274. https://doi.org/10.1007/s10796-014-9489-2
Wu, Q., Zhou, L., Chen, Y., & Chen, H. (2019). An integrated approach to green supplier selection based on the interval type-2 fuzzy best-worst and extended VIKOR methods. Information Sciences, 502, 394–417. https://doi.org/10.1016/j.ins.2019.06.049
Wu, Q., Liu, X., Qin, J., & Zhou, L. (2021a). Multi-criteria group decision-making for portfolio allocation with consensus reaching process under interval type-2 fuzzy environment. Information Sciences, 570, 668–688. https://doi.org/10.1016/j.ins.2021.04.096
Wu, Q., Liu, X., Qin, J., Wang, W., & Zhou, L. (2021b). A linguistic distribution behavioral multi-criteria group decision making model integrating extended generalized TODIM and quantum decision theory. Applied Soft Computing, 98, 106757. https://doi.org/10.1016/j.asoc.2020.106757
Yager, R. R. (2014). Pythagorean membership grades in multi-criteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005
Yang, K., Duan, T., Feng, J., & Mishra, A. R. (2021). Internet of things challenges of sustainable supply chain management in the manufacturing sector using an integrated q-Rung Orthopair Fuzzy-CRITIC-VIKOR method. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-06-2021-0261
Yee-Loong Chong, A., Liu, M. J., Luo, J., & Keng-Boon, O. (2015). Predicting RFID adoption in healthcare supply chain from the perspectives of users. International Journal of Production Economics, 159, 66–75. https://doi.org/10.1016/j.ijpe.2014.09.034
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10
Zeng, S., Hu, Y., & Xie, X. (2021). Q-rung orthopair fuzzy weighted induced logarithmic distance measures and their application in multiple attribute decision making. Engineering Applications of Artificial Intelligence, 100, 104167. https://doi.org/10.1016/j.engappai.2021.104167
Zhang, L., Feng, Y., Shen, P., Zhu, G., Wei, W., Song, J., Ali Shah, S. A., & Bennamoun, M. (2018). Efficient finer-grained incremental processing with MapReduce for big data. Future Generation Computer Systems, 80, 102–111. https://doi.org/10.1016/j.future.2017.09.079
Zielonka, A., Sikora, A., Woźniak, M., Wei, W., Ke, Q., & Bai, Z. (2021). Intelligent Internet of Things system for smart home optimal convection. IEEE Transactions on Industrial Informatics, 17(6), 4308–4317. https://doi.org/10.1109/TII.2020.3009094
Žižović, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126–137. https://doi.org/10.31181/dmame1902102z
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Badia-Melis, R., Mc Carthy, U., Ruiz-Garcia, L., Garcia-Hierro, J., & Robla Villalba, J. I. (2018). New trends in cold chain monitoring applications – A review. Food Control, 86, 170–182. https://doi.org/10.1016/j.foodcont.2017.11.022
Bardaki, C., Kourouthanassis, P., & Pramatari, K. (2012). Deploying RFID-enabled services in the retail supply chain: Lessons learned toward the Internet of Things. Information Systems Management, 29(3), 233–245. https://doi.org/10.1080/10580530.2012.687317
Bauk, S., Drbakovi, M., & Schmeink, A. J. P. (2017). Challenges of tagging goods in supply chains and a cloud perspective with focus on some transitional economies. Transportation, 29(1), 109–120. https://doi.org/10.7307/ptt.v29i1.2162
Belinski, R., Peixe, A. M. M., Frederico, G. F., & Garza-Reyes, J. A. (2020). Organizational learning and Industry 4.0: Findings from a systematic literature review and research agenda. Benchmarking: An International Journal, 27(8), 2435–2457. https://doi.org/10.1108/BIJ-04-2020-0158
Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742. https://doi.org/10.1080/00207543.2017.1402140
Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39–61. https://doi.org/10.1108/SCM-03-2018-0142
Bogle, I. D. L. (2017). A Perspective on smart process manufacturing research challenges for process systems engineers. Engineering, 3(2), 161–165. https://doi.org/10.1016/J.ENG.2017.02.003
Boos, D., Guenter, H., Grote, G., & Kinder, K. (2013). Controllable accountabilities: The Internet of Things and its challenges for organisations. Behaviour & Information Technology, 32(5), 449–467. https://doi.org/10.1080/0144929X.2012.674157
Büyüközkan, G., & Güler, M. (2020). Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique. Measurement, 153, 107353. https://doi.org/10.1016/j.measurement.2019.107353
Cavalcante, E., Pereira, J., Alves, M. P., Maia, P., Moura, R., Batista, T., Delicato, F. C., & Pires, P. F. (2016a). On the interplay of Internet of Things and cloud computing: A systematic mapping study. Computer Communications, 89–90, 17–33. https://doi.org/10.1016/j.comcom.2016.03.012
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016b). Computational intelligence and financial markets: A survey and future directions. Expert Systems With Applications, 55, 194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Cavallaro, F. (2010). Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Applied Energy, 87(2), 496–503. https://doi.org/10.1016/j.apenergy.2009.07.009
Cheng, S., Jianfu, S., Alrasheedi, M., Saeidi, P., Mishra, A. R., & Rani, P. (2021). A New extended VIKOR approach using q-rung orthopair fuzzy sets for sustainable enterprise risk management assessment in manufacturing small and medium-sized enterprises. International Journal of Fuzzy Systems, 23, 1347–1369. https://doi.org/10.1007/s40815-020-01024-3
De Cremer, D., Nguyen, B., & Simkin, L. (2017). The integrity challenge of the Internet-of-Things (IoT): On understanding its dark side. Journal of Marketing Management, 33(1–2), 145–158. https://doi.org/10.1080/0267257X.2016.1247517
Dehnavi, A., Aghdam, I. N., Pradhan, B., & Morshed Varzandeh, M. H. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA, 135, 122–148. https://doi.org/10.1016/j.catena.2015.07.020
Díaz, M., Martín, C., & Rubio, B. (2016). State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing. Journal of Network and Computer Applications, 67, 99–117. https://doi.org/10.1016/j.jnca.2016.01.010
Docherty, I., Marsden, G., & Anable, J. (2018). The governance of smart mobility. Transportation Research Part A: Policy and Practice, 115, 114–125. https://doi.org/10.1016/j.tra.2017.09.012
Dutton, W. H. (2014). Putting things to work: social and policy challenges for the Internet of things. info, 16(3), 1–21. https://doi.org/10.1108/info-09-2013-0047
Dweekat, A. J., Hwang, G., & Park, J. (2017). A supply chain performance measurement approach using the internet of things. Industrial Management & Data Systems, 117(2), 267–286. https://doi.org/10.1108/IMDS-03-2016-0096
Eling, M., & Schnell, W. (2016). What do we know about cyber risk and cyber risk insurance? The Journal of Risk Finance, 17(5), 474–491. https://doi.org/10.1108/JRF-09-2016-0122
Eurich, M., Oertel, N., & Boutellier, R. (2010). The impact of perceived privacy risks on organizations’ willingness to share item-level event data across the supply chain. Electronic Commerce Research, 10, 423–440. https://doi.org/10.1007/s10660-010-9062-0
Fan, H., Li, G., Sun, H., & Cheng, T. C. E. (2017). An information processing perspective on supply chain risk management: Antecedents, mechanism, and consequences. International Journal of Production Economics, 185, 63–75. https://doi.org/10.1016/j.ijpe.2016.11.015
Friedewald, M., & Raabe, O. (2011). Ubiquitous computing: An overview of technology impacts. Telematics and Informatics, 28(2), 55–65. https://doi.org/10.1016/j.tele.2010.09.001
Ghanbari, A., Laya, A., Alonso-Zarate, J., & Markendahl, J. (2017). Business development in the Internet of Things: A matter of vertical cooperation. IEEE Communications Magazine, 55(2), 135–141. https://doi.org/10.1109/MCOM.2017.1600596CM
Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32–47. https://doi.org/10.1016/j.comcom.2014.07.013
Gu, F., Ma, B., Guo, J., Summers, P. A., & Hall, P. (2017). Internet of things and Big Data as potential solutions to the problems in waste electrical and electronic equipment management: An exploratory study. Waste Management, 68, 434–448. https://doi.org/10.1016/j.wasman.2017.07.037
Gu, Y., & Liu, Q. (2013). Research on the application of the internet of things in reverse logistics information management. Journal of Industrial Engineering and Management, 6(4), 963–973. https://doi.org/10.3926/jiem.793
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
Guo, T., Yu, K., Srivastava, G., Wei, W., Guo, L., & Xiong, N. N. (2021). Latent discriminative low-rank projection for visual dimension reduction in green Internet of Things. IEEE Transactions on Green Communications and Networking, 5(2), 737–749. https://doi.org/10.1109/TGCN.2021.3062972
Harris, I., Wang, Y., & Wang, H. (2015). ICT in multimodal transport and technological trends: Unleashing potential for the future. International Journal of Production Economics, 159, 88–103. https://doi.org/10.1016/j.ijpe.2014.09.005
He, J., Huang, Z., Mishra, A. R., & Alrasheedi, M. (2021). Developing a new framework for conceptualizing the emerging sustainable community-based tourism using an extended interval-valued Pythagorean fuzzy SWARA-MULTIMOORA. Technological Forecasting and Social Change, 171, 120955. https://doi.org/10.1016/j.techfore.2021.120955
He, L., Xue, M., & Gu, B. (2020). Internet-of-things enabled supply chain planning and coordination with big data services: Certain theoretic implications. Journal of Management Science and Engineering, 5(1), 1–22. https://doi.org/10.1016/j.jmse.2020.03.002
Ho-Sam-Sooi, N., Pieters, W., & Kroesen, M. (2021). Investigating the effect of security and privacy on IoT device purchase behaviour. Computers & Security, 102, 102132. https://doi.org/10.1016/j.cose.2020.102132
Iordache, M., Schitea, D., Deveci, M., Akyurt, İ. Z., & Iordache, I. (2019). An integrated ARAS and interval type-2 hesitant fuzzy sets method for underground site selection: Seasonal hydrogen storage in salt caverns. Journal of Petroleum Science and Engineering, 175, 1088–1098. https://doi.org/10.1016/j.petrol.2019.01.051
Jing, Q., Vasilakos, A. V., Wan, J., Lu, J., & Qiu, D. (2014). Security of the Internet of Things: Perspectives and challenges. Wireless Networks, 20, 2481–2501. https://doi.org/10.1007/s11276-014-0761-7
Karabasevic, D., Paunkovic, J., & Stanujkic, D. (2016a). Ranking of companies according to the indicators of corporate social responsibility based on SWARA and ARAS methods. Serbian Journal of Management, 11(1), 43–53. https://doi.org/10.5937/sjm11-7877
Karabasevic, D., Zavadskas, E. K., Turskis, Z., & Stanujkic, D. (2016b). The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. Informatica, 27(1), 49–65. https://doi.org/10.15388/Informatica.2016.76
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Data quality in internet of things: A state-of-the-art survey. Journal of Network and Computer Applications, 73, 57–81. https://doi.org/10.1016/j.jnca.2016.08.002
Keršulienė, 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
Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411. https://doi.org/10.1016/j.future.2017.11.022
Krishankumar, R., Ravichandran, K. S., Kar, S., Cavallaro, F., Zavadskas, E. K., & Mardani, A. (2019). Scientific decision framework for evaluation of renewable energy sources under q-rung orthopair fuzzy set with partially known weight information. Sustainability, 11(15), 4202. https://doi.org/10.3390/su11154202
Krishankumar, R., Nimmagadda, A. S., Rani, P., Mishra, A. R., Ravichandran, K. S., & Gandomi, A. H. (2021). Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach. Journal of Cleaner Production, 279, 123329. https://doi.org/10.1016/j.jclepro.2020.123329
Kshetri, N. (2017). Blockchain’s roles in strengthening cybersecurity and protecting privacy. Telecommunications Policy, 41(10), 1027–1038. https://doi.org/10.1016/j.telpol.2017.09.003
Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440. https://doi.org/10.1016/j.bushor.2015.03.008
Leone, L. (2017). Beyond connectivity: The Internet of Food architecture between ethics and the EU citizenry. Journal of Agricultural and Environmental Ethics, 30, 423–438. https://doi.org/10.1007/s10806-017-9675-6
Li, J., Feng, G., Wei, W., Luo, C., Cheng, L., Wang, H., Song, H., & Ming, Z. (2018). PSOTrack: A RFID-based system for random moving objects tracking in unconstrained indoor environment. IEEE Internet of Things Journal, 5(6), 4632–4641. https://doi.org/10.1109/JIOT.2018.2795893
Li, J., Maiti, A., Springer, M., & Gray, T. (2020). Blockchain for supply chain quality management: Challenges and opportunities in context of open manufacturing and industrial internet of things. International Journal of Computer Integrated Manufacturing, 33(12), 1321–1355. https://doi.org/10.1080/0951192X.2020.1815853
Li, J., Zhang, G., Wei, W., Wang, Z., & Zhang, J. (2013). Analysis of wireless link characteristics in RFID location-network. Information Technology Journal, 12(11), 2207–2212. https://doi.org/10.3923/itj.2013.2207.2212
Li, Q., Luo, H., Xie, P.-X., Feng, X.-Q., & Du, R.-Y. (2015). Product whole life-cycle and omni-channels data convergence oriented enterprise networks integration in a sensing environment. Computers in Industry, 70, 23–45. https://doi.org/10.1016/j.compind.2015.01.011
Liu, F., Tan, C.-W., Lim, E. T. K., & Choi, B. (2017). Traversing knowledge networks: An algorithmic historiography of extant literature on the Internet of Things (IoT). Journal of Management Analytics, 4(1), 3–34. https://doi.org/10.1080/23270012.2016.1214540
Liu, P., & Liu, W. Q. (2019). Multiple-attribute group decision-making based on power Bonferroni operators of linguistic q-rung orthopair fuzzy numbers. International Journal of Intelligent Systems, 34(4), 652–689. https://doi.org/10.1002/int.22071
Liu, P. Liu, P., Wang, P., & Zhu, B. (2019). An extended multiple attribute group decision making method based on q-rung orthopair fuzzy numbers. IEEE Access, 7, 162050–162061. https://doi.org/10.1109/ACCESS.2019.2951357
Liu, P., & Wang, P. (2018). Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems, 33(2), 259–280. https://doi.org/10.1002/int.21927
Lowry, P. B., Dinev, T., & Willison, R. (2017). Why security and privacy research lies at the centre of the information systems (IS) artefact: Proposing a bold research agenda. European Journal of Information Systems, 26(6), 546–563. https://doi.org/10.1057/s41303-017-0066-x
Mathaba, S., Adigun, M., Oladosu, J., & Oki, O. (2017). On the use of the Internet of Things and Web 2.0 in inventory management. Journal of Intelligent & Fuzzy Systems, 32(4), 3091–3101. https://doi.org/10.3233/JIFS-169252
Meng, L. (2021). Using IoT in supply chain risk management, to enable collaboration between business, community, and government. Smart Cities, 4(3), 995–1003. https://doi.org/10.3390/smartcities4030052
Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516. https://doi.org/10.1016/j.adhoc.2012.02.016
Mishra, A., Sisodia, G., Raj Pardasani, K., & Sharma, K. (2020a). Multi-criteria IT personnel selection on intuitionistic fuzzy information measures and ARAS methodology. Iranian Journal of Fuzzy Systems, 17, 55–68.
Mishra, A. R., Rani, P., Pandey, K., Mardani, A., Streimikis, J., Streimikiene, D., & Alrasheedi, M. (2020b). Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability, 12(10), 4155. https://doi.org/10.3390/su12104155
Mishra, A. R., Rani, P., Krishankumar, R., Ravichandran, K. S., & Kar, S. (2021). An extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of Coronavirus Disease 2019 (COVID-19). Applied Soft Computing, 103, 107155. https://doi.org/10.1016/j.asoc.2021.107155
Musa, A., & Dabo, A. A. A. (2016). A review of RFID in supply chain management: 2000–2015. Global Journal of Flexible Systems Management, 17, 189–228. https://doi.org/10.1007/s40171-016-0136-2
Neirotti, P., Raguseo, E., & Paolucci, E. (2018). How SMEs develop ICT-based capabilities in response to their environment. Journal of Enterprise Information Management, 31(1), 10–37. https://doi.org/10.1108/JEIM-09-2016-0158
Ochoa, S. F., Fortino, G., & Di Fatta, G. (2017). Cyber-physical systems, internet of things and big data. Future Generation Computer Systems, 75, 82–84. https://doi.org/10.1016/j.future.2017.05.040
Pamucar, D., & Ecer, F. (2020). Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method- FUCOM-F. Facta Universitatis, Series: Mechanical Engineering, 18(3), 419–437. https://doi.org/10.22190/FUME200602034P
Pamucar, D., Stevic, Z., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in MCDM models: Full Consistency Method (FUCOM). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393
Parry, G. C., Brax, S. A., Maull, R. S., & Ng, I. C. L. (2016). Operationalising IoT for reverse supply: The development of use-visibility measures. Supply Chain Management: An International Journal, 21(2), 228–244. https://doi.org/10.1108/SCM-10-2015-0386
Peng, X. (2019). Algorithm for Pythagorean fuzzy multi-criteria decision making based on WDBA with new score function. Fundamenta Informaticae, 165(2), 99–137. https://doi.org/10.3233/FI-2019-1778
Peng, X., & Liu, L. (2019). Information measures for q-rung orthopair fuzzy sets. International Journal of Intelligent Systems, 34(8), 1795–1834. https://doi.org/10.1002/int.22115
Qi, S., Lu, Y., Wei, W., & Chen, X. (2021). Efficient data access control with fine-grained data protection in cloud-assisted IIoT. IEEE Internet of Things Journal, 8(4), 2886–2899. https://doi.org/10.1109/JIOT.2020.3020979
Qiu, X., Luo, H., Xu, G., Zhong, R., & Huang, G. Q. (2015). Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP). International Journal of Production Economics, 159, 4–15. https://doi.org/10.1016/j.ijpe.2014.09.001
Rani, P., & Mishra, A. R. (2020). Multi-criteria weighted aggregated sum product assessment framework for fuel technology selection using q-rung orthopair fuzzy sets. Sustainable Production and Consumption, 24, 90–104. https://doi.org/10.1016/j.spc.2020.06.015
Rani, P., Mishra, A. R., Pardasani, K. R., Mardani, A., Liao, H., & Streimikiene, D. (2019) A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India. Journal of Cleaner Production, 238, 117936. https://doi.org/10.1016/j.jclepro.2019.117936
Rani, P., Mishra, A. R., Saha, A., & Pamucar, D. (2021). Pythagorean fuzzy weighted discrimination‐based approximation approach to the assessment of sustainable bioenergy technologies for agricultural residues. International Journal of Intelligent Systems, 36(6), 2964–2990. https://doi.org/10.1002/int.22408
Ren, J., Guo, H., Xu, C., & Zhang, Y. (2017). Serving at the edge: A scalable IoT architecture based on transparent computing. IEEE Network, 31(5), 96–105. https://doi.org/10.1109/MNET.2017.1700030
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rogers, P. R., Miller, A., & Judge, W. Q. (1999). Using information-processing theory to understand planning/performance relationships in the context of strategy. Strategic Management Journal, 20(6), 567–577. https://doi.org/10.1002/(SICI)1097-0266(199906)20:6<567::AID-SMJ36>3.0.CO;2-K
Rymaszewska, A., Helo, P., & Gunasekaran, A. (2017). IoT powered servitization of manufacturing – an exploratory case study. International Journal of Production Economics, 192, 92–105. https://doi.org/10.1016/j.ijpe.2017.02.016
Saaty, T. L. (1980). The analytical hierarchy process. McGraw-Hill.
Saaty, T. L. (1999, August). Fundamentals of analytic network process. In Proceedings of the International Symposium on the Analytic Hierarchy Process (pp. 348–379). Japan, Kobe. https://doi.org/10.13033/isahp.y1999.038
Stanujkic, D. (2015). Extension of the ARAS method for decision-making problems with interval-valued triangular fuzzy numbers. Informatica, 26(2), 335–355. https://doi.org/10.15388/Informatica.2015.51
Strange, R., & Zucchella, A. (2017). Industry 4.0, global value chains and international business. Multinational Business Review, 25(3), 174–184. https://doi.org/10.1108/MBR-05-2017-0028
Strous, L., von Solms, S., & Zúquete, A. (2021). Security and privacy of the Internet of Things. Computers & Security, 102, 102148. https://doi.org/10.1016/j.cose.2020.102148
Tang, G., Chiclana, F., & Liu, P. (2020). A decision-theoretic rough set model with q-rung orthopair fuzzy information and its application in stock investment evaluation. Applied Soft Computing, 91, 106212. https://doi.org/10.1016/j.asoc.2020.106212
Thomas, R. (2014). In modern supply chains, the soft stuff is the hard stuff. International Journal of Physical Distribution & Logistics Management, 44(6), 1–10. https://doi.org/10.1108/IJPDLM-05-2014-0100
Tu, M. (2018). An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management. The International Journal of Logistics Management, 29(1), 131–151. https://doi.org/10.1108/IJLM-11-2016-0274
Turskis, Z., & Zavadskas, E. K. (2010). A novel method for multiple criteria analysis: Grey additive ratio assessment (ARAS-G) method. Informatica, 21(4), 597–610. https://doi.org/10.15388/Informatica.2010.307
Wang, J., Wei, W., Wang, W., & Li, R. (2018). RFID hybrid positioning method of phased array antenna based on neural network. IEEE Access, 6, 74953–74960. https://doi.org/10.1109/ACCESS.2018.2877396
Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The Internet of Things – A survey of topics and trends. Information Systems Frontiers, 17, 261–274. https://doi.org/10.1007/s10796-014-9489-2
Wu, Q., Zhou, L., Chen, Y., & Chen, H. (2019). An integrated approach to green supplier selection based on the interval type-2 fuzzy best-worst and extended VIKOR methods. Information Sciences, 502, 394–417. https://doi.org/10.1016/j.ins.2019.06.049
Wu, Q., Liu, X., Qin, J., & Zhou, L. (2021a). Multi-criteria group decision-making for portfolio allocation with consensus reaching process under interval type-2 fuzzy environment. Information Sciences, 570, 668–688. https://doi.org/10.1016/j.ins.2021.04.096
Wu, Q., Liu, X., Qin, J., Wang, W., & Zhou, L. (2021b). A linguistic distribution behavioral multi-criteria group decision making model integrating extended generalized TODIM and quantum decision theory. Applied Soft Computing, 98, 106757. https://doi.org/10.1016/j.asoc.2020.106757
Yager, R. R. (2014). Pythagorean membership grades in multi-criteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yager, R. R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230. https://doi.org/10.1109/TFUZZ.2016.2604005
Yang, K., Duan, T., Feng, J., & Mishra, A. R. (2021). Internet of things challenges of sustainable supply chain management in the manufacturing sector using an integrated q-Rung Orthopair Fuzzy-CRITIC-VIKOR method. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-06-2021-0261
Yee-Loong Chong, A., Liu, M. J., Luo, J., & Keng-Boon, O. (2015). Predicting RFID adoption in healthcare supply chain from the perspectives of users. International Journal of Production Economics, 159, 66–75. https://doi.org/10.1016/j.ijpe.2014.09.034
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10
Zeng, S., Hu, Y., & Xie, X. (2021). Q-rung orthopair fuzzy weighted induced logarithmic distance measures and their application in multiple attribute decision making. Engineering Applications of Artificial Intelligence, 100, 104167. https://doi.org/10.1016/j.engappai.2021.104167
Zhang, L., Feng, Y., Shen, P., Zhu, G., Wei, W., Song, J., Ali Shah, S. A., & Bennamoun, M. (2018). Efficient finer-grained incremental processing with MapReduce for big data. Future Generation Computer Systems, 80, 102–111. https://doi.org/10.1016/j.future.2017.09.079
Zielonka, A., Sikora, A., Woźniak, M., Wei, W., Ke, Q., & Bai, Z. (2021). Intelligent Internet of Things system for smart home optimal convection. IEEE Transactions on Industrial Informatics, 17(6), 4308–4317. https://doi.org/10.1109/TII.2020.3009094
Žižović, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126–137. https://doi.org/10.31181/dmame1902102z