Categorizing Users of Cloud Services

Published Online:https://doi.org/10.1287/serv.2016.0128

References

  • Al-Busaidi KA (2009) The impact of learning management system characteristics and user characteristics on the acceptance of e-learning. Internat. J. Global Management Stud. 1:75–91.Google Scholar
  • Arbaugh JB, Duray R (2002) Technological and structural characteristics, student learning and satisfaction with Web-based courses: An exploratory study of two on-line MBA programs. Management Learn. 33(3):331–347.CrossrefGoogle Scholar
  • Aurier P, Jean S, Zaichkowsky JL (2000) Consideration set size and familarity with usage context. Adv. Consumer Res. 27:307–313.Google Scholar
  • Bhattacherjee A, Premkumar G (2004) Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quart. 28:229–254.CrossrefGoogle Scholar
  • Billings DM, Skiba DJ, Connors HR (2005) Best practices in Web-based courses: Generational differences across undergraduate and graduate nursing students. J. Professional Nursing 21:126–133.CrossrefGoogle Scholar
  • Cheng KH, Tsai CC (2011) An investigation of Taiwan University students’ perceptions of online academic help seeking, and their Web-based learning self-efficacy. Internet Higher Ed. 14(3):150–157.CrossrefGoogle Scholar
  • Cho V, Cheng TCE, Lai WMJ (2009) The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Comput. Ed. 53:216–227.CrossrefGoogle Scholar
  • Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 13:319–340.CrossrefGoogle Scholar
  • Doherty W (2006) An analysis of multiple factors affecting retention in Web-based community college courses. Internet Higher Ed. 9(4): 245–255.CrossrefGoogle Scholar
  • Fischer E (1907) Sur la convergence en moyenne. CR Acad. Sci. Paris 144:1022–1024.Google Scholar
  • Ho CH (2010) Continuance intention of e-learning platform: Toward an integrated model. Internat. J. Electronic Bus. Management 8:206–215.Google Scholar
  • Ho LA, Kuo TH (2010) How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Comput. Human Behav. 26:23–31.CrossrefGoogle Scholar
  • Hong KS (2002) Relationships between students’ and instructional variables with satisfaction and learning from a Web-based course. Internet Higher Ed. 5(3):267–281.CrossrefGoogle Scholar
  • Hung MC, Chang IC, Hwang HG (2011) Exploring academic teachers continuance toward the Web-based learning system: The role of causal attributions. Comput. Ed. 57(2):1530–1543.CrossrefGoogle Scholar
  • Islam AKMN (2013) Investigating e-learning system usage outcomes in the university context. Comput. Ed. 69:387–399.CrossrefGoogle Scholar
  • Ituma A (2011) An evaluation of students’ perceptions and engagement with e-learning components in a campus based university. Active Learn. Higher Ed. 12:57–68.CrossrefGoogle Scholar
  • Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognition Lett. 31(8):651–666.CrossrefGoogle Scholar
  • Kleinbaum DG, Klein M (1996) Survival Analysis (Springer, New York).CrossrefGoogle Scholar
  • Klimeck G, Vasileska D (2009) ABACUS and AQME: Semiconductor device and quantum mechanics education on nanohub.org. Comput. Electronics, 2009. IWCE’09. 13th Internat. Workshop (IEEE, Beijing), 1–4.CrossrefGoogle Scholar
  • Klimeck G, McLennan M, Brophy SP, Adams GB, Lundstrom MS (2008) nanoHUB.org: Advancing education and research in nanotechnology. Comput. Sci. Engrg. 10(5):17–23.CrossrefGoogle Scholar
  • Lee MC (2010) Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation-confirmation model. Comput. Ed. 54:506–516.CrossrefGoogle Scholar
  • Lundstrom M, Klimeck G (2006) The NCN: Science, simulation, and cyber services. Emerging Technologies-Nanoelectronics, 2006 IEEE Conf. (IEEE, Singapore), 496–500.CrossrefGoogle Scholar
  • Madhavan K, Zentner M, Klimeck G (2013a) Learning and research in the cloud. Nature Nanotechnology 8:786–789.CrossrefGoogle Scholar
  • Madhavan K, Zentner L, Farnsworth V, Shivarajapura S, Zentner M, Denny N, Klimeck G (2013b) nanoHUB.org: Cloud-based services for nanoscale modeling, simulation, and education. Nanotechnology Rev. 2(1):107–117.CrossrefGoogle Scholar
  • Margolin D, Ognyanoya K, Huang M, Huang Y, Contractor N (2012) Team formation and performance on nanoHUB: A network selection challenge in scientific communities. Vedres B, Scotti M, eds. Networks in Social Policy Problems (Cambridge University Press, Cambridge, MA), 80–100.Google Scholar
  • McLennan M, Kennell R (2010) HUBzero: A platform for dissemination and collaboration in computational science and engineering. Comput. Sci. Engrg. 12(2):48–53.CrossrefGoogle Scholar
  • Moore J, Dickson-Deane C, Galyen K (2011) e-Learning, online learning, and distance learning environments: Are they the same? Internet Higher Ed. 14:129–135.CrossrefGoogle Scholar
  • Network for Computational Nanotechnology (NCN) (2015) nanoHub. Accessed July 1, 2015, http://nanoHUB.org.Google Scholar
  • Nohadani O, Zheng A (2015) Robust multi-objective clustering. Working paper, Northwestern University, Evanston, IL.Google Scholar
  • Ozkan S, Koseler R (2009) Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Comput. Ed. 53:1285–1296.CrossrefGoogle Scholar
  • Pituch KA, Lee YK (2006) The influence of system characteristics on e-learning use. Comput. Ed. 47(2):222–244.CrossrefGoogle Scholar
  • Qiao W, McLennan M, Kennell R, Ebert DS, Klimeck G (2006) Hub-based simulation and graphics hardware accelerated visualization for nanotechnology applications. IEEE Trans. Visualization Comput. Graphics 12(5):1061–1068.CrossrefGoogle Scholar
  • Riesz F (1907) Sur les systèmes orthogonaux de fonctions. CR Acad. Sci. Paris 144:615–619.Google Scholar
  • Saadé R, Kira D (2009) Computer anxiety in e-learning: The effect of computer self-efficacy. J. Inform. Tech. Ed.: Res. 8(1):177–191.CrossrefGoogle Scholar
  • Saadé R, Nebebe F, Tan W (2007) Viability of the “technology acceptance model” in multimedia learning environments: A comparative study. Interdisciplinary J. E-Learn. Learn. Objects 3(1):175–184.CrossrefGoogle Scholar
  • Sam HK, Othman AEA, Nordin ZS (2005) Computer self-efficacy, computer anxiety, and attitudes toward the internet: A study among undergraduates in Unimas. Ed. Tech. Soc. 8:205–219.Google Scholar
  • Shih PC, Muñoz D, Sánchez F (2006) The effect of previous experience with information and communication technologies on performance in a Web-based learning program. Comput. Human Behav. 22(6):962–970.CrossrefGoogle Scholar
  • Simeon T, Aikens CM, Tejerina B, Schatz GC (2011) Northwestern University initiative for teaching nanosciences (NUITNS): An approach for teaching computational chemistry to engineering undergraduate students. J. Chemical Ed. 88(8):1079–1084.CrossrefGoogle Scholar
  • Stockle CO (1992) Canopy photosynthesis and transpiration estimates using radiation interception models with different levels of detail. Ecological Modelling 60(1):31–44.CrossrefGoogle Scholar
  • Strachan A, Klimeck G, Lundstrom M (2010) Cyber-enabled simulations in nanoscale science and engineering. Comput. Sci. Engrg. 12(2):12–17.CrossrefGoogle Scholar
  • 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. Comput. Human Behav. 27:2067–2077.CrossrefGoogle Scholar
  • Sun PC, Tsai RJ, Finger G, Chen YY, Yeh D (2008) What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Comput. Ed. 50(4):1183–1202.CrossrefGoogle Scholar
  • Szajna B (1996) Empirical evaluation of the revised technology acceptance model. Management Sci. 42(1):85–92.LinkGoogle Scholar
  • Twigg CA (2003) Improving learning and reducing costs: New models for online learning. Educause Rev. 38:28–38.Google Scholar
  • Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Sci. 46(2):186–204.LinkGoogle Scholar
  • Zhang D, Nunamaker JF (2003) Powering e-learning in the new millennium: An overview of e-learning and enabling technology. Inform. Systems Frontiers 5:207–218.CrossrefGoogle Scholar
  • Zhao Y, Zhu Q (2010) Influence factors of technology acceptance model in mobile learning. Fourth Internat. Conf. Genetic and Evolutionary Comput (ICGEC) (IEEE, Beijing), 542–545.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.