Education and Information Technologies

, Volume 24, Issue 1, pp 79–102 | Cite as

Decision to adopt online collaborative learning tools in higher education: A case of top Malaysian universities

  • Elaheh YadegaridehkordiEmail author
  • Liyana Shuib
  • Mehrbakhsh NilashiEmail author
  • Shahla Asadi


Recently cloud computing has received significant attention, but its adoption is still far from reaching its full potential, especially in educational contexts. Only a few studies have considered the students’ behavior toward adoption of cloud technology in particular for online collaborative learning purposes. Therefore, this research seeks to develop an adoption model for online collaborative learning tools in cloud environment. To this end, Technology Acceptance Model (TAM) is extended by adding mobility, collaboration, and personalization as external variables. A sample of 209 respondents is collected from four top Malaysian universities and Structural Equation Modelling (SEM) is utilized to assess the research model. The findings show that intention to adopt is significantly affected by perceived usefulness. Although, perceived ease of use does not perform a direct impact on intention to adopt, its indirect influence through perceived usefulness is supported. Mobility and personalization significantly influence perceived ease of use, but they have insignificant impacts on perceived usefulness. Furthermore, perceived usefulness and perceived ease of use are significantly influenced by collaboration. This study rounds off with discussion and conclusions, highlighting implications. The findings provide a baseline for cloud service providers and education institutions in providing effective online collaborative learning tools.


Online collaborative learning tools Mobility Collaboration Personalization Cloud computing 



  1. Ab Hamid, N. R., Akhir, R. M., & Nazir, S. W. M. (2015). Net-generation education: Are we ready. The Macrotheme Review, 4(2), 76–89.Google Scholar
  2. Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238–256. Scholar
  3. Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16–23.Google Scholar
  4. Alharthi, A., Alassafi, M. O., Walters, R. J., & Wills, G. B. (2017). An exploratory study for investigating the critical success factors for cloud migration in the Saudi Arabian higher education context. Telematics and Informatics, 34(2), 664–678. Scholar
  5. Al-Samarraie, H., & Saeed, N. (2018). A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment. Computers & Education, 124, 77–91. Scholar
  6. Ambraziene, D., Miseviciene, R., & Budnikas, G. (2011). Application of cloud computing at KTU: MS live@ Edu case. Informatics in Education an International Journal, 10(2), 259–270.Google Scholar
  7. Baas, P. (2010). Task-technology fit in the workplace (Affecting employee satisfaction and productivity). Rotterdam- Netherlands: (MSc Business Administration), Erasmus University.Google Scholar
  8. Babin, B., Hair, J. F., Hair, J., Anderson, R., & Black, W. C. (2010). Multivariate data analysis: A global perspective (7 ed.). New Jersey: Prentice Hall PTR.Google Scholar
  9. Bansal, S., Singh, S., & Kumar, A. (2012). Use of cloud computing in academic institutions. International Journal of Computer Science and Technology (IJCST), 3(1).Google Scholar
  10. Behrend, T. S., Wiebe, E. N., London, J. E., & Johnson, E. C. (2011). Cloud computing adoption and usage in community colleges. Behaviour & Information Technology, 30(2), 231–240.Google Scholar
  11. Blom, J. (2002). A theory of personalized recommendations. Paper presented at the Human factors in computing systems, New York, NY, USA.Google Scholar
  12. Bouyer, A., & Arasteh, B. (2014). The necessity of using cloud computing in educational system. Procedia - Social and Behavioral Sciences, 143, 581–585.Google Scholar
  13. Brodahl, C., Hadjerrouit, S., & Hansen, N. K. (2011). Collaborative writing with web 2.0 technologies: Education students’ perceptions. Journal of Information Technology Education, 10, 73–103.Google Scholar
  14. Brohi, S. N., & Bamiah, M. A. (2011). Challenges and benefits for adopting the paradigm of cloud computing. (IJAEST) International Journal of Advanced Engineering Sciences and Technologies, 8(2), 286–290.Google Scholar
  15. Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010). Predicting collaboration technology use: Integrating technology adoption and collaboration research. Journal of Management Information Systems, 27(2), 9–54.Google Scholar
  16. Buyya, R., Yeo, C. S., & Venugopal, S. (2008). Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. Paper presented at the 10th International Conference on High Performance Computing and Communications, Dalian.Google Scholar
  17. Calisir, F., Gumussoy, C. A., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web-based learning system: Perceived content quality, anxiety, perceived system quality, I mage, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515–531.Google Scholar
  18. Chadwick, D. W., & Fatema, K. (2012). A privacy preserving authorisation system for the cloud. Journal of Computer and System Sciences, 78(5), 1359–1373. Scholar
  19. Cheol, P. S., & Yul, R. S. (2013). An empirical investigation of end-users’ switching toward cloud computing: A two factor theory perspective. Computers in Human Behavior, 29(1), 160–170.Google Scholar
  20. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175.Google Scholar
  21. Cheung, R., & Vogel, D. (2014). Activity theory as a design framework for collaborative learning using Google applications technology New Horizons in Web Based Learning (pp. 140–149). Berlin: Springer.Google Scholar
  22. Chin, W. W. (1998). The partial least squares approach to structural equation modeling Modern methods for business research (Vol. 2, pp. 295–336). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  23. Chu, T. H., & Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37–52.Google Scholar
  24. Chu, S. K. W., Zhang, Y., Chen, K., Chan, C. K., Lee, C. W. Y., Zou, E., & Lau, W. (2017). The effectiveness of wikis for project-based learning in different disciplines in higher education. The Internet and Higher Education, 33, 49–60. Scholar
  25. Cisco. (2014). Cloud Computing in Higher Education: A Guide to Evaluation and Adoption. Retrieved from
  26. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New Jersey: Lawrence Erlbaum.zbMATHGoogle Scholar
  27. Cronbach, L. (1971). Test validation. Educational Measurement: Issues and Practice, 2, 443–507.Google Scholar
  28. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.Google Scholar
  29. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.Google Scholar
  30. Dennis, A. R., Venkatesh, V., & Ramesh, V. (2003). Adoption of collaboration technologies: Integrating technology acceptance and collaboration technology research. Working Papers on Information Systems, 3(8), 3–8.Google Scholar
  31. Dogruel, L., Joeckel, S., & Bowman, N. D. (2015). The use and acceptance of new media entertainment technology by elderly users: Development of an expanded technology acceptance model. Behaviour & Information Technology, 34(11), 1052–1063.Google Scholar
  32. Du, J., Lu, J., Wu, D., Li, H., & Li, J. (2013). User acceptance of software as a service: Evidence from customers of China's leading e-commerce company, Alibaba. Journal of Systems and Software, 86(8), 2034–2044. Scholar
  33. Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70–88.Google Scholar
  34. Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron: University of Akron Press.Google Scholar
  35. Fan, Y.-W., Wu, C.-C., Chen, C.-D., & Fang, Y.-H. (2015). The effect of status quo Bias on cloud system adoption. The Journal of Computer Information Systems, 55(3), 55.Google Scholar
  36. Faqih, K. M. S. (2016). An empirical analysis of factors predicting the behavioral intention to adopt internet shopping technology among non-shoppers in a developing country context: Does gender matter? Journal of Retailing and Consumer Services, 30, 140–164. Scholar
  37. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (JMR), 34(2), 161–188.Google Scholar
  38. Geisser, S., & Eddy, W. F. (1979). A predictive approach to model selection. Journal of the American Statistical Association, 74(365), 153–160.MathSciNetzbMATHGoogle Scholar
  39. González-Martínez, J. A., Bote-Lorenzo, M. L., Gómez-Sánchez, E., & Cano-Parra, R. (2015). Cloud computing and education: A state-of-the-art survey. Computers & Education, 80, 132–151.Google Scholar
  40. Guo, H., Chen, J., Wu, W., & Wang, W. (2009). Personalization as a service: The architecture and a case study. Paper presented at the Proceedings of the first international workshop on Cloud data management.Google Scholar
  41. Hair Jr., J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage Publications.zbMATHGoogle Scholar
  42. Hair, J., Hult, G. T., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM) (First ed.). London: SAGE Publication.Google Scholar
  43. Hariri, A., & Roberts, P. (2015). Adoption of innovation within universities: Proposing and testing an initial model. Creative Education, 6(2), 186.Google Scholar
  44. Hoyle, R. H. (1999). Structural equation modeling analysis with small samples using partial least squares Statistical Strategies for Small Sample Research (pp. 307–341). London: Sage Publications.Google Scholar
  45. Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information Management, 41(7), 853–868.Google Scholar
  46. Hu, K.-C., Yen, Y.-L., & Chia, K.-C. (2012). Applying UTAUT Model To Explore The Antecedents of Behavioral Intentions for Using Cloud Computing Services: A Case of Software As A Service. Paper presented at the International Conference on Business and Information, Japan.Google Scholar
  47. Huang, Y. M. (2015). Exploring the factors that affect the intention to use collaborative technologies: The differing perspectives of sequential/global learners. Australasian Journal of Educational Technology, 31(3).
  48. Huang, J.-H., Lin, Y.-R., & Chuang, S.-T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585–598.Google Scholar
  49. Ibrahim, M. S., Salleh, N., & Misra, S. (2015). Empirical studies of cloud computing in education: A systematic literature review. Paper presented at the In International Conference on Computational Science and Its Applications, Cham.Google Scholar
  50. Ikäläinen, S. (2013). Collaboration software adoption: Factors affecting adoption of collaboration software in organizations. Information Systems Science Master’s thesis, Department of Information and Service, School of Business, Economy Aalto University.Google Scholar
  51. Ishtaiwa, F. F., & Aburezeq, I. M. (2015). The impact of Google docs on student collaboration: A UAE case study. Learning, Culture and Social Interaction, 7, 85–96. Scholar
  52. Jeong, H., & Hwa-Hong, B. (2012). Service based Personalized Learning System in Cloud Computing Environment. Paper presented at the International Conference on Advanced Computer Science Applications and Technologies, Kuala Lumpur, Malaysia.Google Scholar
  53. Kesharwani, A., & Bisht, S. S. (2012). The impact of trust and perceived risk on internet banking adoption in India. The International Journal of Bank Marketing, 30(4), 303–322. Scholar
  54. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information Management, 43(6), 740–755.Google Scholar
  55. Koch, F., Assunção, M. D., Cardonha, C., & Netto, M. A. S. (2016). Optimising resource costs of cloud computing for education. Future Generation Computer Systems, 55, 473–479. Scholar
  56. Lee, K. (2017). Rethinking the accessibility of online higher education: A historical review. The Internet and Higher Education, 33, 15–23. Scholar
  57. Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61(0), 193–208. Scholar
  58. Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819–827. Scholar
  59. Liao, Y.-W., Huang, Y.-M., Chen, H.-C., & Huang, S.-H. (2015). Exploring the antecedents of collaborative learning performance over social networking sites in a ubiquitous learning context. Computers in Human Behavior, 43, 313–323.Google Scholar
  60. Lin, T.-C., & Huang, C.-C. (2008). Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information Management, 45, 410–417.Google Scholar
  61. Liu, F., Dedehayir, O., & Katzy, B. (2015). Coalition formation during technology adoption. Behaviour & Information Technology, 34(12), 1186–1199.Google Scholar
  62. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334.Google Scholar
  63. Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1–13.Google Scholar
  64. McLoughlin, C., & Lee, M. J. W. (2010). Personalised and self regulated learning in the web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1), 28–43.Google Scholar
  65. Mell, P., & Grance, T. (2009). Effectively and securely using the cloud computing paradigm. NIST, Information Technology Laboratory. Accessed Nov 2017.
  66. Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of Standards and Technology. Accessed Sept 2017.
  67. Miseviciene, R., Budnikas, G., & Ambraziene, D. (2011). Application of cloud computing at KTU: MS live@Edu case. Informatics in Education, 10(2), 259–270.Google Scholar
  68. Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.Google Scholar
  69. Monaco, M., & Martin, M. (2007). The millennial student: A new generation of learners. Athletic Training Education Journal, 2, 42–46.Google Scholar
  70. Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a world-wide-web context. Information Management, 38(4), 217–230.MathSciNetGoogle Scholar
  71. Morphew, C., & Swanson, C. (2011). On the efficacy of raising your university’s rankings. In J. C. Shin, R. K. Toutkoushian, & U. Teichler (Eds.), University rankings (Vol. 3, pp. 185–199). Netherlands: Springer.Google Scholar
  72. Odeh, M., Warwick, K., & Garcia-Perez, A. (2015). The impacts of cloud computing adoption at higher education institutions: A SWOT analysis. International Journal of Computer Applications, 127(4), 15–21.Google Scholar
  73. Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information Management, 51(5), 497–510. Scholar
  74. Orehovački, T., & Babić, S. (2014). Predicting Students’ Continuance Intention Related to the Use of Collaborative Web 2.0 Applications. Paper presented at the Information Systems Development: Transforming Organisations and Society through Information Systems (ISD2014 Proceedings).Google Scholar
  75. Pardeshi, V. H. (2014). Cloud computing for higher education institutes: Architecture, strategy and recommendations for effective adaptation. Procedia Economics and Finance, 11, 589–599.Google Scholar
  76. Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: Exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376–385. Scholar
  77. Park, S.-T., Im, H., & Noh, K.-S. (2016). A study on factors affecting the adoption of LTE mobile communication service: The case of South Korea. Wireless Personal Communications, 86(1), 217–237.Google Scholar
  78. Peng, G., & Gala, C. (2014). Cloud ERP: A new dilemma to modern organisations? The Journal of Computer Information Systems, 54(4), 22–30.Google Scholar
  79. Peng, D. X., & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467–480.Google Scholar
  80. Qin, L., Hsu, J., & Stern, M. (2016). Evaluating the usage of cloud-based collaboration services through teamwork. Journal of Education for Business, 91(4), 227–235. Scholar
  81. Raut, R. D., Priyadarshinee, P., Gardas, B. B., & Jha, M. K. (2018). Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach. Technological Forecasting and Social Change.
  82. Rienzo, T., & Han, B. (2009). Teaching Tip Microsoft or Google Web 2.0 tools for course management. Journal of Information Systems Education, 20(2), 123–127.Google Scholar
  83. Sabi, H. M., Uzoka, F.-M. E., Langmia, K., & Njeh, F. N. (2016). Conceptualizing a model for adoption of cloud computing in education. International Journal of Information Management, 36(2), 183–191. Scholar
  84. Schneckenberg, D. (2014). Easy, collaborative and engaging–the use of cloud computing in the design of management classrooms. Educational Research, 56(4), 412–435.Google Scholar
  85. Sclater, N. (2010). Cloud computing in education. Moscow: UNESCO Institute for Information Technologies in Education.Google Scholar
  86. Sharma, S. K., Joshi, A., & Sharma, H. (2016). A multi-analytical approach to predict the Facebook usage in higher education. Computers in Human Behavior, 55, 340–353.Google Scholar
  87. Shin, D.-H. (2015). User value design for cloud courseware system. Behaviour & Information Technology, 34(5), 506–519.Google Scholar
  88. Shropshire, J., Warkentin, M., & Sharma, S. (2015). Personality, attitudes, and intentions: Predicting initial adoption of information security behavior. Computers & Security, 49, 177–191.Google Scholar
  89. Shyu, S. H.-P., & Huang, J.-H. (2011). Elucidating usage of e-government learning: A perspective of the extended technology acceptance model. Government Information Quarterly, 28(4), 491–502. Scholar
  90. Stevenson, M., & Hedberg, J. G. (2013). Learning and design with online real-time collaboration. Educational Media International, 50(2), 120–134.Google Scholar
  91. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B, Methodological, 36, 111–147.Google Scholar
  92. Straub, D., Boudreau, M. C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(24), 380–427.Google Scholar
  93. ŠUmak, B., HeričKo, M., & PušNik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077.Google Scholar
  94. Sun, G., & Shen, J. (2016). Towards organizing smart collaboration and enhancing teamwork performance: A GA-supported system oriented to mobile learning through cloud-based online course. International Journal of Machine Learning and Cybernetics, 7(3), 391–409. Scholar
  95. Suwantarathip, O., & Wichadee, S. (2014). The effects of collaborative writing activity using Google docs on students’ writing abilities. Turkish Online Journal of Educational Technology, 13(2), 148.Google Scholar
  96. Tan, X., & Kim, Y. (2015). User acceptance of SaaS-based collaboration tools: A case of Google docs. Journal of Enterprise Information Management, 28(3), 423–442. Scholar
  97. Tashkandi, A. N., & Al-Jabri, I. M. (2015). Cloud computing adoption by higher education institutions in Saudi Arabia: An exploratory study. Cluster Computing, 18(4), 1527–1537.Google Scholar
  98. Taylor, C. W., & Hunsinger, D. S. (2011). A study of student use of cloud computing applications. Journal of Information Technology Management, 22(3), 36–50.Google Scholar
  99. Thomas, P. Y. (2011). Cloud computing: A potential paradigm for practising the scholarship of teaching and learning. Electronic Library, 29(2), 214–224. Scholar
  100. Tout, S., Sverdlik, W., & Lawver, G. (2009). Cloud computing and its security in higher education. Proceedings of ISECON, v26 (Washington DC), 2314.Google Scholar
  101. Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology, Theory and Applications, 11(2), 5–40.Google Scholar
  102. van de Weerd, I., Mangula, I. S., & Brinkkemper, S. (2016). Adoption of software as a service in Indonesia: Examining the influence of organizational factors. Information Management, 53(7), 915–928. Scholar
  103. Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology Toward a Unified View. MIS Quarterly, 27(3), 425–478.Google Scholar
  104. White, B. J., Brown, J. A. E., Deale, C. S., & Hardin, A. T. (2009). Collaboration Using Cloud Computing and Traditional Systems. Issues in Information Systems (IIS), X(2).Google Scholar
  105. Yadegaridehkordi, E. A., Iahad, N., & Ahmad, N. (2014). Task-technology fit and user adoption of cloud-based collaborative learning technologies. Paper presented at the international conference of computer and information sciences (ICCOINS), Kuala Lumpur, Malaysia.Google Scholar
  106. Yadegaridehkordi, E., Iahad, N. A., & Ahmad, N. (2015). User perceptions of the technology characteristics in a cloud-based collaborative learning environment: A qualitative study. International Journal of Technology Enhanced Learning, 7(1), 75–90.Google Scholar
  107. Yadegaridehkordi, E., Iahad, N. A., & Ahmad, N. (2016). Task-technology fit assessment of cloud-based collaborative learning technologies. International Journal of Information Systems in the Service Sector (IJISSS), 8(3), 16.Google Scholar
  108. Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M., & Ibrahim, O. (2018a). Predicting determinants of hotel success and development using structural equation modelling (SEM)-ANFIS method. Tourism Management, 66, 364–386. Scholar
  109. Yadegaridehkordi, E., Nizam Bin Md Nasir, M. H., Fazmidar Binti Mohd Noor, N., Shuib, L., & Badie, N. (2018b). Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches. Applied Soft Computing, 66, 77–89. Scholar
  110. Yeou, M. (2016). An investigation of students’ acceptance of Moodle in a blended learning setting using technology acceptance model. Journal of Educational Technology Systems, 44(3), 300–318.Google Scholar
  111. Yoon, H. S., & Occeña, L. U. I. S. (2014). Impacts of customers' perceptions on internet banking use with asmart phone. Journal of Computer Information Systems, 54(3), 1–9.Google Scholar

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Authors and Affiliations

  1. 1.Department of Information Systems, Faculty of Computer Science & Information TechnologyUniversity of Malaya (UM)Kuala LumpurMalaysia
  2. 2.Faculty of ComputingUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  3. 3.Faculty of Computer Science and Information TechnologyUniversiti Putra Malaysia (UPM)SerdangMalaysia

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