How to cite this paper
Baffoe, B., Luo, W., Pan, Q., Zhou, S., Wu, M., Atimu, L., Darko, P & Opoku-Mensah, E. (2023). Assessing the factors for humanitarian logistics digital business ecosystem (HLDBE) using a novel integrated correlation coefficient and standard deviation - combined compromise solution (CCSD-CoCoSo) method.Decision Science Letters , 12(1), 117-136.
Refrences
Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. Harvard business review, 84(4), 98.
Ahn, B. S., & Park, K. S. (2008). Comparing methods for multiattribute decision making with ordinal weights. Computers & Operations Research, 35(5), 1660–1670. https://doi.org/10.1016/j.cor.2006.09.026
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
Bag, S., Luthra, S., Venkatesh, V. G., & Yadav, G. (2020). Towards understanding key enablers to green humanitarian supply chain management practices. Management of Environmental Quality: An International Journal, 31(5), 1111–1145. https://doi.org/10.1108/MEQ-06-2019-0124
Baharmand, H., & Comes, T. (2019). Leveraging Partnerships with Logistics Service Providers in Humanitarian Supply Chains by Blockchain-based Smart Contracts. IFAC-PapersOnLine, 52(13), 12–17. https://doi.org/10.1016/j.ifacol.2019.11.084
Bealt, J., Fernández Barrera, J. C., & Mansouri, S. A. (2016). Collaborative relationships between logistics service providers and humanitarian organizations during disaster relief operations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 118–144. https://doi.org/10.1108/JHLSCM-02-2015-0008
Besiou, M., Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2018). OR applied to humanitarian operations. In European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.02.046
Budak, A., Kaya, İ., Karaşan, A., & Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing, 92, 106322. https://doi.org/10.1016/j.asoc.2020.106322
Chapiro, C., & Bedi, S. (2021, June 8). Leveraging Blockchain for Financial Inclusion. https://www.unicef.org/innovation/InnovationFund/blockchain-financial-inclusion-cohort
Chari, F., Ngcamu, B. S., & Novukela, C. (2020). Supply chain risks in humanitarian relief operations: A case of Cyclone Idai relief efforts in Zimbabwe. Journal of Humanitarian Logistics and Supply Chain Management, 11(1), 29–45. https://doi.org/10.1108/JHLSCM-12-2019-0080
Cozzolino, A. (2021). Platforms Enhancing the Engagement of the Private Sector in Humanitarian Relief Operations. Sustainability, 13(6), 3024. https://doi.org/10.3390/su13063024
Deng, H., Yeh, C.-H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963–973. https://doi.org/10.1016/S0305-0548(99)00069-6
Dilmegani, C. (2020, July 9). 15 AI Applications/ Use Cases / Examples in Logistics in 2021. AI Multiple. https://research.aimultiple.com/logistics-ai/
Doyle, J. R., Green, R. H., & Bottomley, P. A. (1997). Judging Relative Importance: Direct Rating and Point Allocation Are Not Equivalent. Organizational Behavior and Human Decision Processes, 70(1), 65–72. https://doi.org/10.1006/obhd.1997.2694
Dubey, R., Altay, N., & Blome, C. (2019). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research, 283(1–2), 159–177. https://doi.org/10.1007/s10479-017-2676-z
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398. https://doi.org/10.1080/00207543.2020.1722860
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30(2), 341–361.
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120–136. https://doi.org/10.1016/j.ijpe.2019.01.023
Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T., & Douglas, M. A. (2018). Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust. The International Journal of Logistics Management, 29(2), 485–512. https://doi.org/10.1108/IJLM-02-2017-0039
Dufour, É., Laporte, G., Paquette, J., & Rancourt, M. (2018). Logistics service network design for humanitarian response in East Africa. Omega, 74, 1–14. https://doi.org/10.1016/j.omega.2017.01.002
Ebinger, F., & Omondi, B. (2020). Leveraging Digital Approaches for Transparency in Sustainable Supply Chains: A Conceptual Paper. Sustainability, 12(15), 6129. https://doi.org/10.3390/su12156129
Foege, J. N., Lauritzen, G. D., Tietze, F., & Salge, T. O. (2019). Reconceptualizing the paradox of openness: How solvers navigate sharing-protecting tensions in crowdsourcing. Research Policy, 48(6), 1323–1339. https://doi.org/10.1016/j.respol.2019.01.013
Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research policy, 43(7), 1239-1249.
Giuffrida, M., & Mangiaracina, R. (2020). Green Practices for Global Supply Chains in Diverse Industrial, Geographical, and Technological Settings: A Literature Review and Research Agenda. Sustainability, 12(23), 10151. https://doi.org/10.3390/su122310151
Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. In Transportation Research Part E: Logistics and Transportation Review. https://doi.org/10.1016/j.tre.2018.03.011
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
Gupta, S., Beninger, S., & Ganesh, J. (2015). A hybrid approach to innovation by social enterprises: Lessons from Africa. Social Enterprise Journal, 11(1), 89–112. https://doi.org/10.1108/SEJ-04-2014-0023
Hanane, A., Brahim, O., & Bouchra, F. (2016). CCSD and TOPSIS methodology for selecting supplier in a paper company. 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), 275–280. https://doi.org/10.1109/CIST.2016.7805055
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018
Ho, L. L., Law, P. L., & Lim, S. F. (2017). Implementing environmental management systems (EMS) in Sarawak: adoption factors. International Journal of Environmental Science & Sustainable Development, 1(2).
Hotho, J., & Girschik, V. (2019). Corporate engagement in humanitarian action: Concepts, challenges, and areas for international business research. Critical Perspectives on International Business, 15(2/3), 201–218. https://doi.org/10.1108/cpoib-02-2019-0015
Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making (Vol. 186). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9
IASC. (2008, January 22). World Economic Forum (WEF) - OCHA Guiding Principles for Public-Private Collaboration in Humanitarian Action. Retrieved March 2020, from https://interagencystandingcommittee.org/system/files/legacy_files/World%20Economic%20Forum%20-%20OCHA%20Guiding%20Principles%20for%20Public-Private%20Collaboration%20in%20Humanitarian%20Action.pdf
Ittmann, H. W. (2020). Lessons gained from four case studies of operations research for sustainable development in South Africa. Central European Journal of Operations Research, 28(4), 1187–1217. https://doi.org/10.1007/s10100-019-00644-x
John, L., Gurumurthy, A., Soni, G., & Jain, V. (2019). Modelling the inter-relationship between factors affecting coordination in a humanitarian supply chain: A case of Chennai flood relief. Annals of Operations Research, 283(1–2), 1227–1258. https://doi.org/10.1007/s10479-018-2963-3
Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111–122.
Kabra, G. A. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250-1261.
Kamstra, J., & Schulpen, L. (2015). Worlds Apart But Much Alike: Donor Funding and the Homogenization of NGOs in Ghana and Indonesia. Studies in Comparative International Development, 50(3), 331–357. https://doi.org/10.1007/s12116-014-9169-8
Kapoor, R. (2014). Collaborating with complementors: What do firms do? In Collaboration and competition in business ecosystems. Emerald Group Publishing Limited.
Kayikci, Y. (2018). Sustainability impact of digitization in logistics. Procedia Manufacturing, 21, 782–789. https://doi.org/10.1016/j.promfg.2018.02.184
Khan, M., Lee, H., & Bae, J. (2019). The Role of Transparency in Humanitarian Logistics. Sustainability, 11(7), 2078. https://doi.org/10.3390/su11072078
Khan, S. A., Chaabane, A., & Dweiri, F. T. (2018). Multi-Criteria Decision-Making Methods Application in Supply Chain Management: A Systematic Literature Review. In V. A. P. Salomon (Ed.), Multi-Criteria Methods and Techniques Applied to Supply Chain Management. InTech. https://doi.org/10.5772/intechopen.74067
Kim, S., Ramkumar, M., & Subramanian, N. (2019). Logistics service provider selection for disaster preparation: A socio-technical systems perspective. Annals of Operations Research, 283(1–2), 1259–1282. https://doi.org/10.1007/s10479-018-03129-3
KIRON, D., PRENTICE, P. K., & FERGUSON, R. B. (2014). The analytics mandate. MITSloan Management Review. https://sloanreview.mit.edu/projects/analytics-mandate/
Kovács, G. A. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506-528.
Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management. https://doi.org/10.1108/09600030710734820
Kühn, A.-L., Stiglbauer, M., & Fifka, M. S. (2018). Contents and Determinants of Corporate Social Responsibility Website Reporting in Sub-Saharan Africa: A Seven-Country Study. Business & Society, 57(3), 437–480. https://doi.org/10.1177/0007650315614234
Kuprenko, V. (2019, July 3). How AI Changes the Logistic Industry. Towards Data Science. https://towardsdatascience.com/how-ai-changes-the-logistic-industry-3d55401778d
Kwapong Baffoe, B. O. and Luo, W. (2020). Humanitarian Relief Sustainability: A Framework of Humanitarian Logistics Digital Business Ecosystem. Transportation Research Procedia, 48, 363–387. doi: 10.1016/j.trpro.2020.08.032.
Lalicic, L., & Weismayer, C. (2021). Consumers’ reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents. Journal of Business Research, 129, 891–901. https://doi.org/10.1016/j.jbusres.2020.11.005
Landis, J. R. (1977). The measurement of observer agreement for categorical data. biometrics , 159 - 174.
Lee, T. H. (2015). Regression analysis of cloud computing adoption for US hospitals.
Lenkenhoff, K. U. (2018). Key challenges of digital business ecosystem development and how to cope with them. Procedia CIRP, 73, 167-172.
Li, C., Zhang, F., Cao, C., Liu, Y., & Qu, T. (2019). Organizational coordination in sustainable humanitarian supply chain: An evolutionary game approach. Journal of Cleaner Production, 219, 291–303. https://doi.org/10.1016/j.jclepro.2019.01.233
Lian, J.-W., Yen, D. C., & Wang, Y.-T. (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management, 34(1), 28–36.
Lomazzo, C. R., & Hydary, M. (2020, February 13). Digicus: Blending the old with the new. https://www.unicef.org/innovation/blockchain/digicus
Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7), 1006-1023. https://doi.org/10.1108/02635571111161262
Marzenna Cichosz. (2018). Digitalization and Competitiveness in the Logistics Service Industry. e-mentor, 5(77), 73-82.
McKinsey, &. C. (2018, January). Digital/McKinsey: Insights. Retrieved March 2020, from Winning in digital ecosystems: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Digital%20McKinsey%20Insights%20Number%203/Digital-McKinsey-Insights-Issue-3-revised.ashx
Miranda, M. Q., Farias, J. S., de Araújo Schwartz, C., & de Almeida, J. P. L. (2016). Technology adoption in diffusion of innovations perspective: Introduction of an ERP system in a non-profit organization. RAI Revista de Administração e Inovação, 13(1), 48–57. https://doi.org/10.1016/j.rai.2016.02.002
Moore, J. F. (1993). Predators and prey: a new ecology of competition. Harvard business review, 71(3), 75-86.
Munyaka, J.-C. B., & Yadavalli, V. S. S. (2021). Using transportation problem in humanitarian supply chain to prepositioned facility locations: A case study in the Democratic Republic of the Congo. International Journal of System Assurance Engineering and Management, 12(1), 199–216. https://doi.org/10.1007/s13198-020-01031-5
Mutebi, H., Ntayi, J. M., Muhwezi, M., & Munene, J. C. K. (2020). Self-organisation, adaptability, organisational networks and inter-organisational coordination: Empirical evidence from humanitarian organisations in Uganda. Journal of Humanitarian Logistics and Supply Chain Management, 10(4), 447–483. https://doi.org/10.1108/JHLSCM-10-2019-0074
Nasereddin, H. H. O., & A L-Khraishah, H. H. H. (2020). BIG DATA TECHNOLOGIES IN SUPPLY CHAIN MANAGEMENT: OPPORTUNITIES, CHALLENGES AND FUTURE TRENDS. International Journal of Management (IJM), 11(6), 171–179. https://doi.org/10.34218/IJM.11.6.2020.016
Noori, N. S., & Weber, C. (2016). Dynamics of coordination-clusters in long-term rehabilitation. Journal of Humanitarian Logistics and Supply Chain Management, 6(3), 296–328. https://doi.org/10.1108/JHLSCM-06-2016-0024
Nurmala, N., de Leeuw, S., & Dullaert, W. (2017). Humanitarian–business partnerships in managing humanitarian logistics. In Supply Chain Management. https://doi.org/10.1108/SCM-07-2016-0262
Nurmala, N., de Vries, J., & de Leeuw, S. (2018). Cross-sector humanitarian–business partnerships in managing humanitarian logistics: An empirical verification. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1449977
OCHA. (2012, June). OCHA on Message: Humanitarian Principles. Retrieved March 2020, from What are Humanitarian Principles?: https://www.unocha.org/sites/dms/Documents/OOM-humanitarianprinciples_eng_June12.pdf
Oloruntoba, R. A. (2009). Customer service in emergency relief chains. International Journal of Physical Distribution & Logistics Management, 39(6), 486-505.
Ouchi, W. G. (1980). Markets, Bureaucracies, and Clans. Administrative Science Quarterly, 25(1), 129. https://doi.org/10.2307/2392231
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108–1118. https://doi.org/10.1016/j.jclepro.2016.03.059
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sage publications, 402-403.
Ragini, J. R. (2018). Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management, 42, 13-24.
Rancourt, M.-È., Cordeau, J.-F., Laporte, G., & Watkins, B. (2015). Tactical network planning for food aid distribution in Kenya. Computers & Operations Research, 56, 68–83. https://doi.org/10.1016/j.cor.2014.10.018
Ravichandran, T., Lertwongsatien, C., & Lertwongsatien, C. (2005). Effect of Information Systems Resources and Capabilities on Firm Performance: A Resource-Based Perspective. Journal of Management Information Systems, 21(4), 237–276. https://doi.org/10.1080/07421222.2005.11045820
Rezaei, J. (2015). A Systematic Review of Multi-criteria Decision-making Applications in Reverse Logistics. Transportation Research Procedia, 10, 766–776. https://doi.org/10.1016/j.trpro.2015.09.030
Roberts, R., & Goodwin, P. (2002). Weight approximations in multi-attribute decision models. Journal of Multi-Criteria Decision Analysis, 11(6), 291–303. https://doi.org/10.1002/mcda.320
Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: The Free Press.
Rogers, E. M. (2003). Diffusion of Innovations (5th Edition ed.). New York: Simon & Schuster.
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
Sabri, Y., Zarei, M. H., & Harland, C. (2019). Using collaborative research methodologies in humanitarian supply chains. Journal of Humanitarian Logistics and Supply Chain Management, 9(3), 371–409. https://doi.org/10.1108/JHLSCM-06-2018-0041
Sanders, N. R., & Ganeshan, R. (2015). Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”. Production and Operations Management, 24(7), 1193–1194. https://doi.org/10.1111/poms.12413
Schmeiss, J., Hoelzle, K., & Tech, R. P. G. (2019). Designing Governance Mechanisms in Platform Ecosystems: Addressing the Paradox of Openness through Blockchain Technology. California Management Review, 62(1), 121–143. https://doi.org/10.1177/0008125619883618
Schnall, R. T.-D. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informatics, 216, 467-471.
Schumann-Bölsche, D. (2018). Information Technology in Humanitarian Logistics and Supply Chain Management. London: Palgrave Macmillan.
Shin, D.-H., 2013. User centric cloud service model in public sectors: Policy implications of cloud services. Government Information Quarterly, 30(2), pp. 194-203.
Smith, D. (2003). Five principles for research ethics. Monitor on psychology, 34(1), 56.
Soneye, A. (2014). An overview of humanitarian relief supply chains for victims of perennial flood disasters in Lagos, Nigeria (2010-2012). Journal of Humanitarian Logistics and Supply Chain Management, 4(2), 179–197. https://doi.org/10.1108/JHLSCM-01-2014-0004
Srinivasan, R., & Swink, M. (2018). An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective. Production and Operations Management, 27(10), 1849–1867. https://doi.org/10.1111/poms.12746
Stanujkic, D., Popovic, G., Zavadskas, E. K., Karabasevic, D., & Binkyte-Veliene, A. (2020). Assessment of Progress towards Achieving Sustainable Development Goals of the “Agenda 2030” by Using the CoCoSo and the Shannon Entropy Methods: The Case of the EU Countries. Sustainability, 12(14), 5717. https://doi.org/10.3390/su12145717
Tashkandi, A. N., & Al-Jabri, I. M. (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: An exploratory study. Cluster Computing. https://doi.org/10.1007/s10586-015-0490-4
Taymaz, S., Iyigun, C., Bayindir, Z. P., & Dellaert, N. P. (2020). A healthcare facility location problem for a multi-disease, multi-service environment under risk aversion. Socio-Economic Planning Sciences, 71, 100755. https://doi.org/10.1016/j.seps.2019.100755
Thomas, A. a. (2006). Disaster relief. Harvard Business Review, 84, 114-122.
Tiwana, A., 2013. Platform ecosystems: Aligning architecture, governance, and strategy.. s.l.:Newnes.
Tongco, M. D. (2007). Purposive sampling as a tool for informant selection. Ethnobotany Research and applications, 5, 147-158.
Topp, L. B. (2004). The external validity of results derived from ecstasy users recruited using purposive sampling strategies. Drug and alcohol dependence, 73(1), 33-40.
Tornatzky, L. G. (1990). The processes of technological innovation. Issues in organization and management series. (Lexington Books) Retrieved October 3, 2018, from http://www. amazon. com/Processes-Technological-Innovation-Organization/Management/dp/0669203483
Tsai, W.-C. a. L.-L. T., 2012. A model of the adoption of radio frequency identification technology: The case of logistics service firms.. Journal of Engineering and Technology Management, 29(1), 131-151.
Tweel, A. (2012). Examining the relationship between technological, organizational, and environmental factors and cloud computing adoption. Northcentral University.
UN General Assembly. (2008). Strengthening of the coordination of emergency humanitarian assistance of the United Nations. Context, 3(9), 3.
UN-DESA, D. f. (2015). Sustainable Development Knowledge Platform. Retrieved August 2018, from Sustainable Developement Goals: https://sustainabledevelopment.un.org/?menu=1300
Valente, T. W., Dyal, S. R., Chu, K.-H., Wipfli, H., & Fujimoto, K. (2015). Diffusion of innovations theory applied to global tobacco control treaty ratification. Social Science & Medicine, 145, 89–97. https://doi.org/10.1016/j.socscimed.2015.10.001
Van Wassenhove, L. (2006). Humanitarian aid logistics: supply chain management in high gear. The Journal of the Operational Research Society, 57(5), 475-489.
Villa, S., Gonçalves, P., & Villy Odong, T. (2017). Understanding the contribution of effective communication strategies to program performance in humanitarian organizations. Journal of Humanitarian Logistics and Supply Chain Management, 7(2), 126–151. https://doi.org/10.1108/JHLSCM-05-2016-0021
VonAchen, P., Smilowitz, K., Raghavan, M., & Feehan, R. (2016). Optimizing community healthcare coverage in remote Liberia. Journal of Humanitarian Logistics and Supply Chain Management, 6(3), 352–371. https://doi.org/10.1108/JHLSCM-03-2016-0006
Waddell, S. a. (1997). Fostering intersectoral partnering: A guide to promoting cooperation among government, business, and civil society actors. IDR Reports, 13(3), 1-26.
Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist: Big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34(4), 249–252.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Wang, J., Wei, G., Wei, C., & Wu, J. (2020). Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment. Defence Technology, 16(5), 1073–1087. https://doi.org/10.1016/j.dt.2019.11.007
Wang, M. W., Lee, O.-K., & Lim, K. H. (2007). Knowledge management systems diffusion in Chinese enterprises: A multi-stage approach with the technology-organization-environment framework. PACIS 2007 Proceedings, 70.
Wang, Y., Zhao, N., Jing, H., Meng, B., & Yin, X. (2016). A Novel Model of the Ideal Point Method Coupled with Objective and Subjective Weighting Method for Evaluation of Surrounding Rock Stability. Mathematical Problems in Engineering, 2016, 1–9. https://doi.org/10.1155/2016/8935156
Wang, Y.-M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51(1–2), 1–12. https://doi.org/10.1016/j.mcm.2009.07.016
Wang, Y.-M., Wang, Y.-S., & Yang, Y.-F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803–815.
WEF. (2016, January). World Economic Forum. Retrieved March 2020, from World Economic Forum White Paper Digital Transformation of Industries: In collaboration withAccenture: http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/wef-dti-logisticswhitepaper-final-january-2016.pdf
Xie, Q. W. (2017). Predictors for e-government adoption: integrating TAM, TPB, trust and perceived risk. The Electronic Library, 35(1), 2-20.
Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019). A GREY COMBINED COMPROMISE SOLUTION (COCOSO-G) METHOD FOR SUPPLIER SELECTION IN CONSTRUCTION MANAGEMENT. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 25(8), 858–874. https://doi.org/10.3846/jcem.2019.11309
Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458
Yejun Xu & Zhijian Cai. (2008). Standard deviation method for determining the weights of group multiple attribute decision making under uncertain linguistic environment. 2008 7th World Congress on Intelligent Control and Automation, 8311–8316. https://doi.org/10.1109/WCICA.2008.4594230
Yılmaz, H., & Kabak, Ö. (2020). Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach. Journal of Enterprise Information Management, 33(5), 1199–1232. https://doi.org/10.1108/JEIM-09-2019-0310
Zavadskas, E. K., Kaklauskas, A., & Kalibatas, D. (2009). AN APPROACH TO MULTI-ATTRIBUTE ASSESSMENT OF INDOOR ENVIRONMENT BEFORE AND AFTER REFURBISHMENT OF DWELLINGS/DAUGIATIKSLIO GYVENAMŲJŲ NAMŲ VIDINĖS APLINKOS VERTINIMO PRIEŠ IR PO RENOVACIJOS BŪDAS/ КОМПЛЕКСНАЯ ОЦЕНКА ВНУТРЕННЕЙ СРЕДЫ ЖИЛЫХ ДОМОВ ДО И ПОСЛЕ РЕНОВАЦИИ. JOURNAL OF ENVIRONMENTAL ENGINEERING AND LANDSCAPE MANAGEMENT, 17(1), 5–11. https://doi.org/10.3846/1648-6897.2009.17.5-11
Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X., & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260–272.
Zhu, K. (2004). The complementarity of information technology infrastructure and e-commerce capability: A resource-based assessment of their business value. Journal of Management Information Systems, 21(1), 167–202.
Ahn, B. S., & Park, K. S. (2008). Comparing methods for multiattribute decision making with ordinal weights. Computers & Operations Research, 35(5), 1660–1670. https://doi.org/10.1016/j.cor.2006.09.026
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
Bag, S., Luthra, S., Venkatesh, V. G., & Yadav, G. (2020). Towards understanding key enablers to green humanitarian supply chain management practices. Management of Environmental Quality: An International Journal, 31(5), 1111–1145. https://doi.org/10.1108/MEQ-06-2019-0124
Baharmand, H., & Comes, T. (2019). Leveraging Partnerships with Logistics Service Providers in Humanitarian Supply Chains by Blockchain-based Smart Contracts. IFAC-PapersOnLine, 52(13), 12–17. https://doi.org/10.1016/j.ifacol.2019.11.084
Bealt, J., Fernández Barrera, J. C., & Mansouri, S. A. (2016). Collaborative relationships between logistics service providers and humanitarian organizations during disaster relief operations. Journal of Humanitarian Logistics and Supply Chain Management, 6(2), 118–144. https://doi.org/10.1108/JHLSCM-02-2015-0008
Besiou, M., Pedraza-Martinez, A. J., & Van Wassenhove, L. N. (2018). OR applied to humanitarian operations. In European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.02.046
Budak, A., Kaya, İ., Karaşan, A., & Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing, 92, 106322. https://doi.org/10.1016/j.asoc.2020.106322
Chapiro, C., & Bedi, S. (2021, June 8). Leveraging Blockchain for Financial Inclusion. https://www.unicef.org/innovation/InnovationFund/blockchain-financial-inclusion-cohort
Chari, F., Ngcamu, B. S., & Novukela, C. (2020). Supply chain risks in humanitarian relief operations: A case of Cyclone Idai relief efforts in Zimbabwe. Journal of Humanitarian Logistics and Supply Chain Management, 11(1), 29–45. https://doi.org/10.1108/JHLSCM-12-2019-0080
Cozzolino, A. (2021). Platforms Enhancing the Engagement of the Private Sector in Humanitarian Relief Operations. Sustainability, 13(6), 3024. https://doi.org/10.3390/su13063024
Deng, H., Yeh, C.-H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963–973. https://doi.org/10.1016/S0305-0548(99)00069-6
Dilmegani, C. (2020, July 9). 15 AI Applications/ Use Cases / Examples in Logistics in 2021. AI Multiple. https://research.aimultiple.com/logistics-ai/
Doyle, J. R., Green, R. H., & Bottomley, P. A. (1997). Judging Relative Importance: Direct Rating and Point Allocation Are Not Equivalent. Organizational Behavior and Human Decision Processes, 70(1), 65–72. https://doi.org/10.1006/obhd.1997.2694
Dubey, R., Altay, N., & Blome, C. (2019). Swift trust and commitment: The missing links for humanitarian supply chain coordination? Annals of Operations Research, 283(1–2), 159–177. https://doi.org/10.1007/s10479-017-2676-z
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398. https://doi.org/10.1080/00207543.2020.1722860
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource‐based view and big data culture. British Journal of Management, 30(2), 341–361.
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Fosso Wamba, S., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120–136. https://doi.org/10.1016/j.ijpe.2019.01.023
Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B. T., & Douglas, M. A. (2018). Big data and predictive analytics in humanitarian supply chains: Enabling visibility and coordination in the presence of swift trust. The International Journal of Logistics Management, 29(2), 485–512. https://doi.org/10.1108/IJLM-02-2017-0039
Dufour, É., Laporte, G., Paquette, J., & Rancourt, M. (2018). Logistics service network design for humanitarian response in East Africa. Omega, 74, 1–14. https://doi.org/10.1016/j.omega.2017.01.002
Ebinger, F., & Omondi, B. (2020). Leveraging Digital Approaches for Transparency in Sustainable Supply Chains: A Conceptual Paper. Sustainability, 12(15), 6129. https://doi.org/10.3390/su12156129
Foege, J. N., Lauritzen, G. D., Tietze, F., & Salge, T. O. (2019). Reconceptualizing the paradox of openness: How solvers navigate sharing-protecting tensions in crowdsourcing. Research Policy, 48(6), 1323–1339. https://doi.org/10.1016/j.respol.2019.01.013
Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. https://doi.org/10.1108/JEIM-08-2013-0065
Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research policy, 43(7), 1239-1249.
Giuffrida, M., & Mangiaracina, R. (2020). Green Practices for Global Supply Chains in Diverse Industrial, Geographical, and Technological Settings: A Literature Review and Research Agenda. Sustainability, 12(23), 10151. https://doi.org/10.3390/su122310151
Govindan, K., Cheng, T. C. E., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. In Transportation Research Part E: Logistics and Transportation Review. https://doi.org/10.1016/j.tre.2018.03.011
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
Gupta, S., Beninger, S., & Ganesh, J. (2015). A hybrid approach to innovation by social enterprises: Lessons from Africa. Social Enterprise Journal, 11(1), 89–112. https://doi.org/10.1108/SEJ-04-2014-0023
Hanane, A., Brahim, O., & Bouchra, F. (2016). CCSD and TOPSIS methodology for selecting supplier in a paper company. 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), 275–280. https://doi.org/10.1109/CIST.2016.7805055
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018
Ho, L. L., Law, P. L., & Lim, S. F. (2017). Implementing environmental management systems (EMS) in Sarawak: adoption factors. International Journal of Environmental Science & Sustainable Development, 1(2).
Hotho, J., & Girschik, V. (2019). Corporate engagement in humanitarian action: Concepts, challenges, and areas for international business research. Critical Perspectives on International Business, 15(2/3), 201–218. https://doi.org/10.1108/cpoib-02-2019-0015
Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Making (Vol. 186). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9
IASC. (2008, January 22). World Economic Forum (WEF) - OCHA Guiding Principles for Public-Private Collaboration in Humanitarian Action. Retrieved March 2020, from https://interagencystandingcommittee.org/system/files/legacy_files/World%20Economic%20Forum%20-%20OCHA%20Guiding%20Principles%20for%20Public-Private%20Collaboration%20in%20Humanitarian%20Action.pdf
Ittmann, H. W. (2020). Lessons gained from four case studies of operations research for sustainable development in South Africa. Central European Journal of Operations Research, 28(4), 1187–1217. https://doi.org/10.1007/s10100-019-00644-x
John, L., Gurumurthy, A., Soni, G., & Jain, V. (2019). Modelling the inter-relationship between factors affecting coordination in a humanitarian supply chain: A case of Chennai flood relief. Annals of Operations Research, 283(1–2), 1227–1258. https://doi.org/10.1007/s10479-018-2963-3
Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111–122.
Kabra, G. A. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250-1261.
Kamstra, J., & Schulpen, L. (2015). Worlds Apart But Much Alike: Donor Funding and the Homogenization of NGOs in Ghana and Indonesia. Studies in Comparative International Development, 50(3), 331–357. https://doi.org/10.1007/s12116-014-9169-8
Kapoor, R. (2014). Collaborating with complementors: What do firms do? In Collaboration and competition in business ecosystems. Emerald Group Publishing Limited.
Kayikci, Y. (2018). Sustainability impact of digitization in logistics. Procedia Manufacturing, 21, 782–789. https://doi.org/10.1016/j.promfg.2018.02.184
Khan, M., Lee, H., & Bae, J. (2019). The Role of Transparency in Humanitarian Logistics. Sustainability, 11(7), 2078. https://doi.org/10.3390/su11072078
Khan, S. A., Chaabane, A., & Dweiri, F. T. (2018). Multi-Criteria Decision-Making Methods Application in Supply Chain Management: A Systematic Literature Review. In V. A. P. Salomon (Ed.), Multi-Criteria Methods and Techniques Applied to Supply Chain Management. InTech. https://doi.org/10.5772/intechopen.74067
Kim, S., Ramkumar, M., & Subramanian, N. (2019). Logistics service provider selection for disaster preparation: A socio-technical systems perspective. Annals of Operations Research, 283(1–2), 1259–1282. https://doi.org/10.1007/s10479-018-03129-3
KIRON, D., PRENTICE, P. K., & FERGUSON, R. B. (2014). The analytics mandate. MITSloan Management Review. https://sloanreview.mit.edu/projects/analytics-mandate/
Kovács, G. A. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506-528.
Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in disaster relief operations. International Journal of Physical Distribution & Logistics Management. https://doi.org/10.1108/09600030710734820
Kühn, A.-L., Stiglbauer, M., & Fifka, M. S. (2018). Contents and Determinants of Corporate Social Responsibility Website Reporting in Sub-Saharan Africa: A Seven-Country Study. Business & Society, 57(3), 437–480. https://doi.org/10.1177/0007650315614234
Kuprenko, V. (2019, July 3). How AI Changes the Logistic Industry. Towards Data Science. https://towardsdatascience.com/how-ai-changes-the-logistic-industry-3d55401778d
Kwapong Baffoe, B. O. and Luo, W. (2020). Humanitarian Relief Sustainability: A Framework of Humanitarian Logistics Digital Business Ecosystem. Transportation Research Procedia, 48, 363–387. doi: 10.1016/j.trpro.2020.08.032.
Lalicic, L., & Weismayer, C. (2021). Consumers’ reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents. Journal of Business Research, 129, 891–901. https://doi.org/10.1016/j.jbusres.2020.11.005
Landis, J. R. (1977). The measurement of observer agreement for categorical data. biometrics , 159 - 174.
Lee, T. H. (2015). Regression analysis of cloud computing adoption for US hospitals.
Lenkenhoff, K. U. (2018). Key challenges of digital business ecosystem development and how to cope with them. Procedia CIRP, 73, 167-172.
Li, C., Zhang, F., Cao, C., Liu, Y., & Qu, T. (2019). Organizational coordination in sustainable humanitarian supply chain: An evolutionary game approach. Journal of Cleaner Production, 219, 291–303. https://doi.org/10.1016/j.jclepro.2019.01.233
Lian, J.-W., Yen, D. C., & Wang, Y.-T. (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management, 34(1), 28–36.
Lomazzo, C. R., & Hydary, M. (2020, February 13). Digicus: Blending the old with the new. https://www.unicef.org/innovation/blockchain/digicus
Low, C., Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7), 1006-1023. https://doi.org/10.1108/02635571111161262
Marzenna Cichosz. (2018). Digitalization and Competitiveness in the Logistics Service Industry. e-mentor, 5(77), 73-82.
McKinsey, &. C. (2018, January). Digital/McKinsey: Insights. Retrieved March 2020, from Winning in digital ecosystems: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Digital%20McKinsey%20Insights%20Number%203/Digital-McKinsey-Insights-Issue-3-revised.ashx
Miranda, M. Q., Farias, J. S., de Araújo Schwartz, C., & de Almeida, J. P. L. (2016). Technology adoption in diffusion of innovations perspective: Introduction of an ERP system in a non-profit organization. RAI Revista de Administração e Inovação, 13(1), 48–57. https://doi.org/10.1016/j.rai.2016.02.002
Moore, J. F. (1993). Predators and prey: a new ecology of competition. Harvard business review, 71(3), 75-86.
Munyaka, J.-C. B., & Yadavalli, V. S. S. (2021). Using transportation problem in humanitarian supply chain to prepositioned facility locations: A case study in the Democratic Republic of the Congo. International Journal of System Assurance Engineering and Management, 12(1), 199–216. https://doi.org/10.1007/s13198-020-01031-5
Mutebi, H., Ntayi, J. M., Muhwezi, M., & Munene, J. C. K. (2020). Self-organisation, adaptability, organisational networks and inter-organisational coordination: Empirical evidence from humanitarian organisations in Uganda. Journal of Humanitarian Logistics and Supply Chain Management, 10(4), 447–483. https://doi.org/10.1108/JHLSCM-10-2019-0074
Nasereddin, H. H. O., & A L-Khraishah, H. H. H. (2020). BIG DATA TECHNOLOGIES IN SUPPLY CHAIN MANAGEMENT: OPPORTUNITIES, CHALLENGES AND FUTURE TRENDS. International Journal of Management (IJM), 11(6), 171–179. https://doi.org/10.34218/IJM.11.6.2020.016
Noori, N. S., & Weber, C. (2016). Dynamics of coordination-clusters in long-term rehabilitation. Journal of Humanitarian Logistics and Supply Chain Management, 6(3), 296–328. https://doi.org/10.1108/JHLSCM-06-2016-0024
Nurmala, N., de Leeuw, S., & Dullaert, W. (2017). Humanitarian–business partnerships in managing humanitarian logistics. In Supply Chain Management. https://doi.org/10.1108/SCM-07-2016-0262
Nurmala, N., de Vries, J., & de Leeuw, S. (2018). Cross-sector humanitarian–business partnerships in managing humanitarian logistics: An empirical verification. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1449977
OCHA. (2012, June). OCHA on Message: Humanitarian Principles. Retrieved March 2020, from What are Humanitarian Principles?: https://www.unocha.org/sites/dms/Documents/OOM-humanitarianprinciples_eng_June12.pdf
Oloruntoba, R. A. (2009). Customer service in emergency relief chains. International Journal of Physical Distribution & Logistics Management, 39(6), 486-505.
Ouchi, W. G. (1980). Markets, Bureaucracies, and Clans. Administrative Science Quarterly, 25(1), 129. https://doi.org/10.2307/2392231
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2017). The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108–1118. https://doi.org/10.1016/j.jclepro.2016.03.059
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sage publications, 402-403.
Ragini, J. R. (2018). Big data analytics for disaster response and recovery through sentiment analysis. International Journal of Information Management, 42, 13-24.
Rancourt, M.-È., Cordeau, J.-F., Laporte, G., & Watkins, B. (2015). Tactical network planning for food aid distribution in Kenya. Computers & Operations Research, 56, 68–83. https://doi.org/10.1016/j.cor.2014.10.018
Ravichandran, T., Lertwongsatien, C., & Lertwongsatien, C. (2005). Effect of Information Systems Resources and Capabilities on Firm Performance: A Resource-Based Perspective. Journal of Management Information Systems, 21(4), 237–276. https://doi.org/10.1080/07421222.2005.11045820
Rezaei, J. (2015). A Systematic Review of Multi-criteria Decision-making Applications in Reverse Logistics. Transportation Research Procedia, 10, 766–776. https://doi.org/10.1016/j.trpro.2015.09.030
Roberts, R., & Goodwin, P. (2002). Weight approximations in multi-attribute decision models. Journal of Multi-Criteria Decision Analysis, 11(6), 291–303. https://doi.org/10.1002/mcda.320
Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: The Free Press.
Rogers, E. M. (2003). Diffusion of Innovations (5th Edition ed.). New York: Simon & Schuster.
Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
Sabri, Y., Zarei, M. H., & Harland, C. (2019). Using collaborative research methodologies in humanitarian supply chains. Journal of Humanitarian Logistics and Supply Chain Management, 9(3), 371–409. https://doi.org/10.1108/JHLSCM-06-2018-0041
Sanders, N. R., & Ganeshan, R. (2015). Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”. Production and Operations Management, 24(7), 1193–1194. https://doi.org/10.1111/poms.12413
Schmeiss, J., Hoelzle, K., & Tech, R. P. G. (2019). Designing Governance Mechanisms in Platform Ecosystems: Addressing the Paradox of Openness through Blockchain Technology. California Management Review, 62(1), 121–143. https://doi.org/10.1177/0008125619883618
Schnall, R. T.-D. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informatics, 216, 467-471.
Schumann-Bölsche, D. (2018). Information Technology in Humanitarian Logistics and Supply Chain Management. London: Palgrave Macmillan.
Shin, D.-H., 2013. User centric cloud service model in public sectors: Policy implications of cloud services. Government Information Quarterly, 30(2), pp. 194-203.
Smith, D. (2003). Five principles for research ethics. Monitor on psychology, 34(1), 56.
Soneye, A. (2014). An overview of humanitarian relief supply chains for victims of perennial flood disasters in Lagos, Nigeria (2010-2012). Journal of Humanitarian Logistics and Supply Chain Management, 4(2), 179–197. https://doi.org/10.1108/JHLSCM-01-2014-0004
Srinivasan, R., & Swink, M. (2018). An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective. Production and Operations Management, 27(10), 1849–1867. https://doi.org/10.1111/poms.12746
Stanujkic, D., Popovic, G., Zavadskas, E. K., Karabasevic, D., & Binkyte-Veliene, A. (2020). Assessment of Progress towards Achieving Sustainable Development Goals of the “Agenda 2030” by Using the CoCoSo and the Shannon Entropy Methods: The Case of the EU Countries. Sustainability, 12(14), 5717. https://doi.org/10.3390/su12145717
Tashkandi, A. N., & Al-Jabri, I. M. (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: An exploratory study. Cluster Computing. https://doi.org/10.1007/s10586-015-0490-4
Taymaz, S., Iyigun, C., Bayindir, Z. P., & Dellaert, N. P. (2020). A healthcare facility location problem for a multi-disease, multi-service environment under risk aversion. Socio-Economic Planning Sciences, 71, 100755. https://doi.org/10.1016/j.seps.2019.100755
Thomas, A. a. (2006). Disaster relief. Harvard Business Review, 84, 114-122.
Tiwana, A., 2013. Platform ecosystems: Aligning architecture, governance, and strategy.. s.l.:Newnes.
Tongco, M. D. (2007). Purposive sampling as a tool for informant selection. Ethnobotany Research and applications, 5, 147-158.
Topp, L. B. (2004). The external validity of results derived from ecstasy users recruited using purposive sampling strategies. Drug and alcohol dependence, 73(1), 33-40.
Tornatzky, L. G. (1990). The processes of technological innovation. Issues in organization and management series. (Lexington Books) Retrieved October 3, 2018, from http://www. amazon. com/Processes-Technological-Innovation-Organization/Management/dp/0669203483
Tsai, W.-C. a. L.-L. T., 2012. A model of the adoption of radio frequency identification technology: The case of logistics service firms.. Journal of Engineering and Technology Management, 29(1), 131-151.
Tweel, A. (2012). Examining the relationship between technological, organizational, and environmental factors and cloud computing adoption. Northcentral University.
UN General Assembly. (2008). Strengthening of the coordination of emergency humanitarian assistance of the United Nations. Context, 3(9), 3.
UN-DESA, D. f. (2015). Sustainable Development Knowledge Platform. Retrieved August 2018, from Sustainable Developement Goals: https://sustainabledevelopment.un.org/?menu=1300
Valente, T. W., Dyal, S. R., Chu, K.-H., Wipfli, H., & Fujimoto, K. (2015). Diffusion of innovations theory applied to global tobacco control treaty ratification. Social Science & Medicine, 145, 89–97. https://doi.org/10.1016/j.socscimed.2015.10.001
Van Wassenhove, L. (2006). Humanitarian aid logistics: supply chain management in high gear. The Journal of the Operational Research Society, 57(5), 475-489.
Villa, S., Gonçalves, P., & Villy Odong, T. (2017). Understanding the contribution of effective communication strategies to program performance in humanitarian organizations. Journal of Humanitarian Logistics and Supply Chain Management, 7(2), 126–151. https://doi.org/10.1108/JHLSCM-05-2016-0021
VonAchen, P., Smilowitz, K., Raghavan, M., & Feehan, R. (2016). Optimizing community healthcare coverage in remote Liberia. Journal of Humanitarian Logistics and Supply Chain Management, 6(3), 352–371. https://doi.org/10.1108/JHLSCM-03-2016-0006
Waddell, S. a. (1997). Fostering intersectoral partnering: A guide to promoting cooperation among government, business, and civil society actors. IDR Reports, 13(3), 1-26.
Waller, M. A., & Fawcett, S. E. (2013). Click here for a data scientist: Big data, predictive analytics, and theory development in the era of a maker movement supply chain. Journal of Business Logistics, 34(4), 249–252.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Wang, J., Wei, G., Wei, C., & Wu, J. (2020). Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment. Defence Technology, 16(5), 1073–1087. https://doi.org/10.1016/j.dt.2019.11.007
Wang, M. W., Lee, O.-K., & Lim, K. H. (2007). Knowledge management systems diffusion in Chinese enterprises: A multi-stage approach with the technology-organization-environment framework. PACIS 2007 Proceedings, 70.
Wang, Y., Zhao, N., Jing, H., Meng, B., & Yin, X. (2016). A Novel Model of the Ideal Point Method Coupled with Objective and Subjective Weighting Method for Evaluation of Surrounding Rock Stability. Mathematical Problems in Engineering, 2016, 1–9. https://doi.org/10.1155/2016/8935156
Wang, Y.-M., & Luo, Y. (2010). Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling, 51(1–2), 1–12. https://doi.org/10.1016/j.mcm.2009.07.016
Wang, Y.-M., Wang, Y.-S., & Yang, Y.-F. (2010). Understanding the determinants of RFID adoption in the manufacturing industry. Technological Forecasting and Social Change, 77(5), 803–815.
WEF. (2016, January). World Economic Forum. Retrieved March 2020, from World Economic Forum White Paper Digital Transformation of Industries: In collaboration withAccenture: http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/wef-dti-logisticswhitepaper-final-january-2016.pdf
Xie, Q. W. (2017). Predictors for e-government adoption: integrating TAM, TPB, trust and perceived risk. The Electronic Library, 35(1), 2-20.
Yazdani, M., Wen, Z., Liao, H., Banaitis, A., & Turskis, Z. (2019). A GREY COMBINED COMPROMISE SOLUTION (COCOSO-G) METHOD FOR SUPPLIER SELECTION IN CONSTRUCTION MANAGEMENT. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 25(8), 858–874. https://doi.org/10.3846/jcem.2019.11309
Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458
Yejun Xu & Zhijian Cai. (2008). Standard deviation method for determining the weights of group multiple attribute decision making under uncertain linguistic environment. 2008 7th World Congress on Intelligent Control and Automation, 8311–8316. https://doi.org/10.1109/WCICA.2008.4594230
Yılmaz, H., & Kabak, Ö. (2020). Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach. Journal of Enterprise Information Management, 33(5), 1199–1232. https://doi.org/10.1108/JEIM-09-2019-0310
Zavadskas, E. K., Kaklauskas, A., & Kalibatas, D. (2009). AN APPROACH TO MULTI-ATTRIBUTE ASSESSMENT OF INDOOR ENVIRONMENT BEFORE AND AFTER REFURBISHMENT OF DWELLINGS/DAUGIATIKSLIO GYVENAMŲJŲ NAMŲ VIDINĖS APLINKOS VERTINIMO PRIEŠ IR PO RENOVACIJOS BŪDAS/ КОМПЛЕКСНАЯ ОЦЕНКА ВНУТРЕННЕЙ СРЕДЫ ЖИЛЫХ ДОМОВ ДО И ПОСЛЕ РЕНОВАЦИИ. JOURNAL OF ENVIRONMENTAL ENGINEERING AND LANDSCAPE MANAGEMENT, 17(1), 5–11. https://doi.org/10.3846/1648-6897.2009.17.5-11
Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X., & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-enabled production data. International Journal of Production Economics, 165, 260–272.
Zhu, K. (2004). The complementarity of information technology infrastructure and e-commerce capability: A resource-based assessment of their business value. Journal of Management Information Systems, 21(1), 167–202.