An Empirical Investigation of Business Intelligence Adoption in Jordanian Healthcare Organizations Using the TOE Framework

Abdallah Moflih, M., Alabaddi, Z., Rahahleh, A., Alali, H., Ahmad Alabaddi, Z., Hisham Rahahleh, A., Abdallah Muflih, M., & Nawaf Al-nsour, A. (2020). The Relative Importance Of The Critical Success Factors Of Business Intelligence(Bi) Systems Implementation In Jordanian Pharmaceutical Companies. Journal of Theoretical and Applied Information Technology, 30, 12. www.jatit.org
Ahmad, A., Ahmad, R., & Hashim, K. F. (2016). Innovation traits for business intelligence successful deployment. Journal of Theoretical and Applied Information Technology, 89(1), 96.
Ahmad Khan, F., Ali Khan, N., & Aslam, A. (2024). Adoption of Artificial Intelligence in Human Resource Management: An Application of TOE-TAM Model. https://www.researchgate.net/publication/380490808
Ahmad, S., Miskon, S., Alabdan, R., & Tlili, I. (2021). Statistical assessment of business intelligence system adoption model for sustainable textile and apparel industry. IEEE Access, 9, 106560–106574.
Ain, N. U., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review. Decision Support Systems, 125, 113113. https://doi.org/10.1016/j.dss.2019.113113
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Al-Dwairi, R. M., Al-Khataybeh, M., Najadat, D., & Rawashdeh, A. (2024). User self-efficacy enhances business intelligence tools for organizational agility. Indonesian Journal of Electrical Engineering and Computer Science, 36(1), 592–602.
Alharbi, F., Atkins, A., & Stanier, C. (2016). Understanding the determinants of Cloud Computing adoption in Saudi healthcare organisations. Complex & Intelligent Systems, 2(3), 155–171. https://doi.org/10.1007/s40747-016-0021-9
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023). A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge, 8(1), 100333.
Ali, O., & Osmanaj, V. (2020). The role of government regulations in the adoption of cloud computing: A case study of local government. Computer Law & Security Review, 36, 105396. https://doi.org/10.1016/j.clsr.2020.105396
Aligarh, F., Sutopo, B., & Widarjo, W. (2023). The antecedents of cloud computing adoption and its consequences for MSMEs’ performance: A model based on the Technology-Organization-Environment (TOE) framework. Cogent Business & Management, 10(2), 2220190.
Alkhalil, A., Sahandi, R., & John, D. (2017). An exploration of the determinants for decision to migrate existing resources to cloud computing using an integrated TOE-DOI model. Journal of Cloud Computing, 6, 1–20.
Alkhwaldi, A. F. (2024). Understanding the acceptance of business intelligence from healthcare professionals’ perspective: An empirical study of healthcare organizations. International Journal of Organizational Analysis.
Almusallam, M., Pradhan, S., & Mastio, E. (2021). Assessment of initial and Post-adoption Factors of Business Intelligence Systems in Saudi’s SMEs A pilot study.
Alzghoul, A., Khaddam, A. A., Abousweilem, F., Irtaimeh, H. J., & Alshaar, Q. (2024). How business intelligence capability impacts decision-making speed, comprehensiveness, and firm performance. Information Development, 40(2), 220–233.
ARNET ZITHA, D. R. (2023). A model for the business intelligence system acceptance in the South African banking sector. Journal of Theoretical and Applied Information Technology, 101(8).
Asongu, S. A., & Biekpe, N. (2017). Government Quality Determinants of ICT Adoption in Sub-Saharan Africa. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3002189
Awa, H. O., & Ojiabo, O. U. (2016). A model of adoption determinants of ERP within TOE framework. Information Technology & People, 29(4), 901–930.
Bag, S., Rahman, M. S., Gupta, S., & Wood, L. C. (2023). Understanding and predicting the determinants of blockchain technology adoption and SMEs’ performance. The International Journal of Logistics Management, 34(6), 1781–1807.
Bany Mohammad, A., Al-Okaily, M., Al-Majali, M., & Masa’deh, R. (2022). Business Intelligence and Analytics (BIA) Usage in the Banking Industry Sector: An Application of the TOE Framework. Journal of Open Innovation: Technology, Market, and Complexity, 8(4). https://doi.org/10.3390/joitmc8040189
Basile, L. J., Carbonara, N., Pellegrino, R., & Panniello, U. (2023). Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making. Technovation, 120, 102482.
Bhatiasevi, V., & Naglis, M. (2020). Elucidating the determinants of business intelligence adoption and organizational performance. Information Development, 36(1), 78–96. https://doi.org/10.1177/0266666918811394
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021). Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technological Forecasting and Social Change, 170, 120880. https://doi.org/10.1016/j.techfore.2021.120880
Chaveesuk, S., & Horkondee, S. (2015). An integrated model of business intelligence adoption in Thailand logistics service firms. 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 604–608.
Chen, Y., & Lin, Z. (2021). Business Intelligence Capabilities and Firm Performance: A Study in China. International Journal of Information Management, 57, 102232. https://doi.org/10.1016/j.ijinfomgt.2020.102232
Chittipaka, V., Kumar, S., Sivarajah, U., Bowden, J. L.-H., & Baral, M. M. (2023). Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework. Annals of Operations Research, 327(1), 465–492.
Cruz-Jesus, F., Pinheiro, A., & Oliveira, T. (2019). Understanding CRM adoption stages: empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13.
Macredie, R., & Mijinyawa, K. (2011). A theory-grounded framework of Open Source Software adoption in SMEs. European Journal of Information Systems, 20(2), 237–250. https://doi.org/10.1057/ejis.2010.60
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1–25.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040
Delen, D., Moscato, G., & Toma, I. L. (2018). The impact of real-time business intelligence and advanced analytics on the behaviour of business decision makers. 2018 International Conference on Information Management and Processing (ICIMP), 49–53.
Djerdjouri, M. (2020). Data and Business Intelligence Systems for Competitive Advantage: prospects, challenges, and real-world applications. Mercados y Negocios, 41, 5–18.
ELDALABEEH, A. R., AL-SHBAIL, M. O., ALMUIET, M. Z., BAKER, M. B., & E’LEIMAT, D. (2021). Cloud-Based Accounting Adoption in Jordanian Financial Sector. Journal of Asian Finance, Economics and Business, 8(2), 833–849. https://doi.org/10.13106/jafeb.2021.vol8.no2.0833
Elena, C. (2011). Business intelligence. Journal of Knowledge Management, Economics and Information Technology, 1(2), 1–12.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fortune Business Insights. (2024). Fortune Business Insight. https://www.fortunebusinessinsights.com/business-intelligence-bi-market-103742
Foshay, N., & Kuziemsky, C. (2014). Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management, 34(1), 20–27. https://doi.org/10.1016/j.ijinfomgt.2013.09.003
Foshay, N., Taylor, A., & Mukherjee, A. (2014). Winning the Hearts and Minds of Business Intelligence Users: The Role of Metadata. Information Systems Management, 31(2), 167–180. https://doi.org/10.1080/10580530.2014.890444
Gangwar, H., & Date, H. (2016). Understanding cloud computing adoption: A model comparison approach. Human Systems Management, 35(2), 93–114. https://doi.org/10.3233/HSM-150857
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
García, J. M. V., & Pinzón, B. H. D. (2017). Key success factors to business intelligence solution implementation. Journal of Intelligence Studies in Business, 7(1). https://doi.org/10.37380/jisib.v7i1.215
George, A., Schmitz, K., & Storey, V. C. (2020). A framework for building mature business intelligence and analytics in organizations. Journal of Database Management, 31(3), 14–39. https://doi.org/10.4018/JDM.2020070102
Grover, V., & Goslar, M. D. (1993). The Initiation, Adoption, and Implementation of Telecommunications Technologies in U.S. Organizations. Journal of Management Information Systems, 10(1), 141–164. https://doi.org/10.1080/07421222.1993.11517994
Gutierrez, A., Boukrami, E., & Lumsden, R. (2015). Technological, organisational and environmental factors influencing managers’ decision to adopt cloud computing in the UK. Journal of Enterprise Information Management, 28(6), 788–807. https://doi.org/10.1108/JEIM-01-2015-0001
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
Hassan, N. R. (2019). The origins of business analytics and implications for the information systems field. Journal of Business Analytics, 2(2), 118–133. https://doi.org/10.1080/2573234X.2019.1693912
Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Zoubi, J. Z. Al. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100111. https://doi.org/10.1016/j.joitmc.2023.100111
Hosen, M. S., Islam, R., Naeem, Z., Folorunso, E. O., Chu, T. S., Al Mamun, M. A., & Orunbon, N. O. (2024). Data-driven decision making: Advanced database systems for business intelligence. Nanotechnology Perceptions, 20(3), 687–704.
Huang, H.-C., Wang, H.-K., Chen, H.-L., Wei, J., Yin, W.-H., & Lin, K.-C. (2024). Adopting Business Intelligence Techniques in Healthcare Practice. Informatics, 11(3), 65. https://doi.org/10.3390/informatics11030065
Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 465–485.
Jalghoum, Y., Tahtamouni, A., Khasawneh, S., & Al-Madadha, A. (2021). Challenges to healthcare information systems development: The case of Jordan. International Journal of Healthcare Management, 14(2), 447–455.
Jalil, N. A., Prapinit, P., Melan, M., & Mustaffa, A. Bin. (2019). Adoption of business intelligence-Technological, individual and supply chain efficiency. 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 67–73.
Jaradat, Z., Al-Dmour, A., Alshurafat, H., Al-Hazaima, H., & Al Shbail, M. O. (2024). Factors influencing business intelligence adoption: evidence from Jordan. Journal of Decision Systems, 33(2), 242–262. https://doi.org/10.1080/12460125.2022.2094531
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2021). Blockchain technology applications for Industry 4.0: A literature-based review. Blockchain: Research and Applications, 2(4), 100027.
Kalema, B. M., & Carol, M. N. (2019). A statistical analysis of business intelligence acceptance by SMEs in the city of Tshwane, Republic of South Africa. Academy of Entrepreneurship Journal, 25(2).
Kašparová, P. (2023). Intention to use business intelligence tools in decision making processes: Applying a UTAUT 2 model. Central European Journal of Operations Research, 31(3), 991–1008.
Katebi, A., Homami, P., & Najmeddin, M. (2022). Acceptance model of precast concrete components in building construction based on technology acceptance model (TAM) and technology, organization, and environment (TOE) framework. Journal of Building Engineering, 45, 103518.
Kesavan, P., & Dy, C. (2020). Impact of healthcare reform on technology and innovation. Hand Clinics, 36(2), 255.
Kester, Q.-A., & Preko, M. (2015). Business intelligence adoption in developing economies: a case study of Ghana. International Journal of Computer Applications, 127(1), 5–11.
Klecun, E. (2016). Transforming healthcare: policy discourses of IT and patient-centred care. European Journal of Information Systems, 25(1), 64–76. https://doi.org/10.1057/ejis.2014.40
Kumar, A., & Krishnamoorthy, B. (2020). Business Analytics Adoption in Firms: A Qualitative Study Elaborating TOE Framework in India. International Journal of Global Business and Competitiveness, 15(2), 80–93. https://doi.org/10.1007/s42943-020-00013-5
Liang, Y., Wang, W., Dong, K., Zhang, G., & Qi, G. (2021). Adoption of mobile government cloud from the perspective of public sector. Mobile Information Systems, 2021(1), 8884594.
Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103–110.
Mahakittikun, T., Suntrayuth, S., & Bhatiasevi, V. (2021). The impact of technological-organizational-environmental (TOE) factors on firm performance: merchant’s perspective of mobile payment from Thailand’s retail and service firms. Journal of Asia Business Studies, 15(2), 359–383.
Maroufkhani, P., Iranmanesh, M., & Ghobakhloo, M. (2023). Determinants of big data analytics adoption in small and medium-sized enterprises (SMEs). Industrial Management & Data Systems, 123(1), 278–301.
Marshall, L., & De la Harpe, R. (2009). Decision making in the context of business intelligence and data quality. South African Journal of Information Management, 11(2).
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169.
Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and E-Business Management, 16, 547–578.
Miranda, J., Miller, S., Alfieri, N., Lalonde, A., Ivan?Ortiz, E., Hanson, C., Steinholt, M., Palshetkar, N., Suharjono, H., & Gebhardt, S. (2024). Global health systems strengthening: FIGO’s strategic view on reducing maternal and newborn mortality worldwide. International Journal of Gynecology & Obstetrics, 165(3), 849–859.
Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022). An extended technology-organization-environment (TOE) framework for online retailing utilization in digital transformation: empirical evidence from vietnam. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 200.
Nithya, N., & Kiruthika, R. (2021). Impact of Business Intelligence Adoption on performance of banks: a conceptual framework. Journal of Ambient Intelligence and Humanized Computing, 12(2), 3139–3150. https://doi.org/10.1007/s12652-020-02473-2
Njenga, K., Garg, L., Bhardwaj, A. K., Prakash, V., & Bawa, S. (2019). The cloud computing adoption in higher learning institutions in Kenya: Hindering factors and recommendations for the way forward. Telematics and Informatics, 38, 225–246. https://doi.org/10.1016/j.tele.2018.10.007
Owusu, A. (2020). Determinants of cloud business intelligence adoption among Ghanaian SMEs. International Journal of Cloud Applications and Computing (IJCAC), 10(4), 48–69.
Papachristodoulou, E., Koutsaki, M., & Kirkos, E. (2017). Business intelligence and SMEs: Bridging the gap. Journal of Intelligence Studies in Business, 7(1).
Park, J.-H., & Kim, Y. B. (2021). Factors activating big data adoption by Korean firms. Journal of Computer Information Systems, 61(3), 285–293.
Popovi?, A., Puklavec, B., & Oliveira, T. (2019). Justifying business intelligence systems adoption in SMEs: Impact of systems use on firm performance. Industrial Management & Data Systems, 119(1), 210–228.
Qatawneh, N. (2024). Empirical insights into business intelligence adoption and decision-making performance during the digital transformation era: Extending the TOE model in the Jordanian banking sector. Journal of Open Innovation: Technology, Market, and Complexity, 10(4), 100401.
Qatawneh, N., Aljaafreh, A., & Al-Laymoun, O. (2024). Business Intelligence Adoption Model During the Digital Transformation Era: An Empirical Investigation in the Jordanian Insurance Companies. In Information and Communication Technology in Technical and Vocational Education and Training for Sustainable and Equal Opportunity: Education, Sustainability and Women Empowerment (pp. 543–552). Springer.
Ramakrishnan, T., Kathuria, A., & Saldanha, T. J. V. (2020). Business intelligence and analytics (BI&A) capabilities in healthcare. In Theory and Practice of Business Intelligence in Healthcare (pp. 1–17). IGI Global Scientific Publishing.
Ramalingam, S., Subramanian, M., Reddy, A. S., Tarakaramu, N., Khan, M. I., Abdullaev, S., & Dhahbi, S. (2024). Exploring business intelligence applications in the healthcare industry: A comprehensive analysis. Egyptian Informatics Journal, 25, 100438.
Ritika Goel, Tanya Karn, Rahul Kushwaha, & Ashima Mehta. (2024). Healthcare Resource Allocation Optimization. International Journal of Advanced Research in Science, Communication and Technology, 429–433. https://doi.org/10.48175/IJARSCT-17569
Rogers, E. M. (1995). Diffusion of innovations, edition of the Free Press. The Fourth.
Rogers, E. M. (2003). Diffusion of Innovations.
Salisu, I., Bin Mohd Sappri, M., & Bin Omar, M. F. (2021). The adoption of business intelligence systems in small and medium enterprises in the healthcare sector: A systematic literature review. Cogent Business and Management, 8(1). https://doi.org/10.1080/23311975.2021.1935663
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115. https://doi.org/10.1016/j.jfbs.2014.01.002
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. john wiley & sons.
Skafi, M., Yunis, M. M., & Zekri, A. (2020). Factors influencing SMEs’ adoption of cloud computing services in Lebanon: An empirical analysis using TOE and contextual theory. IEEE Access, 8, 79169–79181.
Stjepi?, A.-M., Peji? Bach, M., & Bosilj Vukši?, V. (2021). Exploring Risks in the Adoption of Business Intelligence in SMEs Using the TOE Framework. Journal of Risk and Financial Management, 14(2), 58. https://doi.org/10.3390/jrfm14020058
Sutarno, M., & Anam, K. (2022). An Empirical Study on the Use of Digital Technologies to Achieve Cost-Effectiveness in Healthcare Management. American Journal of Health Behavior, 46(6), 781–793.
Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books, 118–147.
Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, EM-29(1), 28–45. https://doi.org/10.1109/TEM.1982.6447463
Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information & Management, 55(7), 822–839.
Umam, B., Darmawan, A. K., Anwari, A., Santosa, I., Walid, M., & Hidayanto, A. N. (2020). Mobile-based Smart Regency Adoption with TOE framework: An empirical inquiry from Madura Island Districts. 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 1–6. https://doi.org/10.1109/ICICoS51170.2020.9299025
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
wael AL-khatib, A. (2023). Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework. Technology in Society, 75, 102403.
Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96–99.
Wieder, B., & Ossimitz, M.-L. (2015). The impact of Business Intelligence on the quality of decision making–a mediation model. Procedia Computer Science, 64, 1163–1171.
Williams, R. A., Duman, G. M., Kongar, E., & Tenney, D. (2024). Understanding Business Intelligence Implementation Failure From Technology, Organization, and Process Perspectives. IEEE Engineering Management Review, 52(1), 151–176. https://doi.org/10.1109/EMR.2023.3331247
Xie, X., Zhang, H., & Blanco, C. (2023). How organizational readiness for digital innovation shapes digital business model innovation in family businesses. International Journal of Entrepreneurial Behavior & Research, 29(1), 49–79.
Yang, J., Chu, S.-C., & Cao, Y. (2024). Adopting AI Advertising Creative Technology in China: A Mixed Method Study Through the Technology-Organization-Environment (TOE) Framework, Perceived Value and Ethical Concerns. Journal of Current Issues & Research in Advertising, 1–24.
Yang, R., Tang, W., & Zhang, J. (2021). Technology improvement strategy for green products under competition: The role of government subsidy. European Journal of Operational Research, 289(2), 553–568.
Yoon, T. E., Ghosh, B., & Jeong, B.-K. (2014). User acceptance of business intelligence (BI) application: Technology, individual difference, social influence, and situational constraints. 2014 47th Hawaii International Conference on System Sciences, 3758–3766.
Yusof, A. F., Miskon, S., Ahmad, N., Alias, R. A., Hashim, H., Abdullah, N. S., Ali, N. M., & Maarof, M. A. (2015). Implementation issues affecting the business intelligence adoption in public university. ARPN J. Eng. Appl. Sci, 10, 18061–18069.
Yusof, E. M. M., Othman, M. S., Yusuf, L. M., Kumaran, S. R., & Yusof, A. R. M. (2019). A model of acceptance factors for business intelligence in manufacturing using theoretical models. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1544–1551.
Zoubi, M., ALfaris, Y., Fraihat, B., Otoum, A., Nawasreh, M., & ALfandi, A. (2023). An extension of the diffusion of innovation theory for business intelligence adoption: A maturity perspective on project management. Uncertain Supply Chain Management, 11(2), 465–472.

Al-Daraba, K., Sharif, S. M., Alshami, S. A., & Alkhasawnehb, R. (2025). An Empirical Investigation of Business Intelligence Adoption in Jordanian Healthcare Organizations Using the TOE Framework. International Journal of Academic Research in Business and Social Sciences, 15(5), 183–206.

Copyright: © 2025 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode