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Education and Information Technologies

, Volume 23, Issue 3, pp 1175–1202 | Cite as

Examining gender issues in perception and acceptance in web-based end-user development activities

  • Tzafilkou KaterinaEmail author
  • Protogeros Nicolaos
Article
  • 171 Downloads

Abstract

In the recent years in the End-User Development (EUD) research there is a shift from the study of tools that focus on desktop graphical applications, to the development of EUD for web environments. Human-Computer Interaction (HCI) research has shown significant gender differences while users interact with EUD systems. However, most of this research focuses on desktop spreadsheet environments. In this paper we examine the potential gender differences in perception and acceptance in modern web-based EUD environments. We step on previous gender research in the fields of EUD and Technology Acceptance to concentrate a set of appropriate items and examine a set of related hypotheses. To check out our research hypotheses we have conducted a field test using a prototype web-based EUD tool based on a natural language approach (named ‘simple talking’), to assist end-users in creating database-driven mobile applications. The results of the field test show significant gender differences in Risk-Perception and Perceived-Ease of Use. As it was predicted, male users perceived significantly higher ease of use and female users perceived significantly higher risk. Gender differences also exist in the correlations between different pairs of perception and acceptance items.

Keywords

End-user development (EUD) Gender in human computer interaction (GenderHCI) Perceived-ease of use Perceived-usefulness Risk-perception Self-efficacy 

References

  1. Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361–391.CrossRefGoogle Scholar
  2. Agarwal, R., Sambamurthy, V., & Stair, R. M. (2000). Research report: the evolving relationship between general and specific computer self-efficacy—an empirical assessment. Information Systems Research, 11(4), 418–430.CrossRefGoogle Scholar
  3. Bandura, A. (1977). Self-efficacy: the exercise of control. New York: Freeman.Google Scholar
  4. Bandura, A. (1986). Social Foundations of Thought and Action: A social Cognitive Theory. Prentice-Hall, Englewood Cliffs, N. J.Google Scholar
  5. Beckwith, L. (2003) Gender HCI issues in end-user software engineering. IEEE Symposium on Human Centric Computing Languages and Environments 2003 Proceedings, pp. 273–274.Google Scholar
  6. Beckwith, L. (2007) Gender HCI issues in end-user programming, Ph.D. Thesis, Oregon State University.Google Scholar
  7. Beckwith, L., & Burnett, M. (2004). “Gender: an important factor in end-user programming environments?” 2004 IEEE Symposium on Visual Languages - Human Centric Computing, pp. 107–114.Google Scholar
  8. Beckwith, L., Sorte, S., Burnett, M., Wiedenbeck, S., Chintakovid, T., & Cook, C. (2005). Designing features for both genders in end-user programming environments. In Proceedings of the 2005 I.E. Symposium on Visual Languages and Human-Centric Computing (VLHCC '05) IEEE computer society, Washington, DC, pp. 153–160.Google Scholar
  9. Beckwith, L., Burnett, M., Grigoreanu, M., & Wiedenbeck, S. (2006a). Gender HCI: what about the software? Computer, Nov. 2006, pp. 83–87.Google Scholar
  10. Beckwith, L., Kissinger, C., Burnett, M., Wiedenbeck, S., Lawrance, J., Blackwell, A., & Cook, C. (2006b). Tinkering and gender in end-user programmers' debugging. In R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, & G. Olson (Eds.), Proceedings of the SIGCHI conference on human factors in computing systems (CHI '06) (pp. 231–240). New York: ACM.CrossRefGoogle Scholar
  11. Beyer, S., Rynes, K., Perrault, J., Hay, K., & Haller, S. (2003) Gender differences in computer science students. SIGCSE: Special Interest Group on Computer Science Education, pp. 49–53.Google Scholar
  12. Blackwell, A. (2002). First steps in programming: a rationale for attention investment models. In IEEE Human-Centric Computing Languages and Environments, pp. 2–10.Google Scholar
  13. Blackwell, A., & Green, T.R.G. (1999). Investment of attention as an analytic approach to cognitive dimensions. In T.R.G. Green, T. Abdullah, & P. Brna (Eds.), 11th Workshop of the Psychology of Programming Interest Group, pp. 24–35.Google Scholar
  14. Blackwell, A. F., Rode, J. A., & Toye, E. F. (2009). How do we program the home? Gender, attention investment, and the psychology of programming at home. International Journal of Human-Computer Studies, 67, 324–341.CrossRefGoogle Scholar
  15. Brosnan, M. (1998). The role of psychological gender in the computer-related attitudes and attainments of primary school children (Aged 6–11). Computers and Education, v30 n3–4 203–08, Apr–May 1998.Google Scholar
  16. Burnett, M. (2009). What is end-user software engineering and why does it matter? End-User Development, pp. 15–28.Google Scholar
  17. Burnett, M. (2010). Gender HCI: what about the software? In Proceedings of the 28th ACM International Conference on Design of Communication (SIGDOC '10) ACM. New York, pp. 251–251.Google Scholar
  18. Burnett, M. M., & Scaffidi, C. (2011). End-user development. In M. Soegaard & R. F. Dam (Eds.), Encyclopedia of human-computer interaction. Aarhus: The Interaction-Design.org Foundation.Google Scholar
  19. Burnett, M., Wiedenbeck, S. Grigoreanu, V., Subrahmaniyan, N., Beckwith, L., & Kissinger, C. (2008). Gender in end-user software engineering. In Proceedings of the 4th international workshop on End-user software engineering (WEUSE '08) ACM, New York. pp. 21–24.Google Scholar
  20. Burnett, M., Fleming, S., Iqbal, S. (2010) Gender differences and programming environments: across programming populations. Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement.Google Scholar
  21. Burnett, M. M., Beckwith, L., Wiedenbeck, S., Fleming, S. D., Cao, J., Park, T. H., Grigoreanu, V., et al. (2011). Gender pluralism in problem-solving software. Interacting with Computers, 23(5), 450–460.CrossRefGoogle Scholar
  22. Burnett, M., Stumpf, S., Macbeth, J., Makri, S., Beckwith, L., Kwan, I., Peters, A., & Jernigan, W. (2016). GenderMag: a method for evaluating Software's gender inclusiveness. Interacting with Computers.  https://doi.org/10.1093/iwc/iwv046.
  23. Cao, J., Rector, K., Park, T., Fleming, S., Burnett, M. & Wiedenbeck, S. (2010). A debugging perspective on end-user mashup programming. In Proc. IEEE visual languages and human-centric computing (pp. 149–156). IEEE: Los Alamitos.Google Scholar
  24. Chang, I.-C., Hwang, H.-G., Hung, W.-F., & Li, Y.-C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296–303.CrossRefGoogle Scholar
  25. Cheng, D., Liu, G., Qian, C. & Song, Y.F. (2008). Customer acceptance of internet banking: integrating trust and quality with UTAUT model. IEEE International Conference on Service Operations and Logistics, and Informatics, Beijing, 12–15 October.Google Scholar
  26. Colley, A., & Comber, C. (2003). Age and gender differences in computer use and attitudes among secondary school students: what has changed? Educational Research, 45(2), 155–165.CrossRefGoogle Scholar
  27. Colquitt, J. A., LePine, J. A., & Noe, R. A. (2000). Toward an integrative theory of training motivation: a meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85, 678–707.CrossRefGoogle Scholar
  28. Compeau, D., Higgins, C. (1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118–143. Computers in Human Behaviour, 17, 21–33.Google Scholar
  29. Costabile, M.F., Fogli, D., Letondal, C., Mussio, P., & Piccinno, A. (2003a). Domain-expert users and their needs of software development. Proc. Special Session on EUD, UAHCI Conference, Crete, Greece, pp. 532–536.Google Scholar
  30. Costabile M.F., Fogli, D., Fresta, G., Mussio, P., & Piccinno, A. (2003b). Building environments for end-user development and tailoring, human centric computing languages and environments, 2003. Proceedings. 2003 I.E. Symposium on, 2003, pp. 31–38.Google Scholar
  31. Cuccurullo, S., Francese, R., Risi, M. & Tortora, G. (2011). MicroApps development on mobile phones. In M. Costabile, Y. Dittrich, G. Fischer, & A. Piccinno, (Eds.), End-User Development, vol. 6654 of Lecture Notes in Computer Science (pp. 289–294). Berlin: Springer.Google Scholar
  32. Danado, J. & Paternò, F. (2012a). A prototype for EUD in touch-based mobile devices. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC '12), pp. 83–86.Google Scholar
  33. Danado, J. & Paternò, F. (2012b) Puzzle: a visual-based environment for end user development in touch-based mobile phone. Human-Centered Software Engineering, vol. 7623 of Lecture Notes in Computer Science, pp. 199–216.Google Scholar
  34. Danado, J., Davies, M., Ricca, P. & Fensel, A. (2010). An authoring tool for user generated mobile services. In A. Berre, A. Gomez-Pérez, K. Tutschku, & D. Fensel (Eds.), Proceedings of the 3rd future internet conference on future internet (FIS '10) (pp. 118–127) Springer.Google Scholar
  35. Davis, F. D. (1989). Perceived-usefulness, perceived-ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.CrossRefGoogle Scholar
  36. De Angeli, A., Battocchi, A. Roy C. S., Rodriguez, C., & Daniel, F., Casati, F. (2011). Conceptual design and evaluation of WIRE: a wisdom-aware EUD tool. Technical Report DISI-11-353, Ingegneria e Scienza dell'Informazione, University of Trento.Google Scholar
  37. Durndell, A., & Thomson, K. (1997). Gender and computing: a decade of change? Computers & Education, 28(1), 1–9.CrossRefGoogle Scholar
  38. Durndell, A., Hagg, Z., & Laithwaite, H. (2000). Computer self-efficacy and gender: a cross cultural study of Scotland and Romania. Personality and Individual Differences, 28(6), 1037–1044.CrossRefGoogle Scholar
  39. Farahat, T. (2012) Applying the technology acceptance model to online learning in the Egyptian universities. Procedia - Social and Behavioural Sciences, Volume 64, 9 November 2012, pp. 95–104.Google Scholar
  40. Finucane, M. L., Slovic, P., Mertz, C. K., Flynn, J., & Satterfield, T. A. (2000). Gender, race, and perceived risk: the 'white male' effect. Health, Risk, & Society, 2(2), 159–172.CrossRefGoogle Scholar
  41. Ghiani, G., Paternò, F. & Spano, L. D. (2011) Creating mashups by direct manipulation of existing web applications. End-User Development, vol. 6654 of Lecture Notes in Computer Science (pp. 42–52). Springer, Berlin.Google Scholar
  42. Ghiani, G., Paternò, F., Spano, L. D., & Pintori, G. (2016). An environment for end-user development of web mashups. International Journal of Human-Computer Studies, 87, March 2016, 38–64.CrossRefGoogle Scholar
  43. Goswami, A., & Dutta, S. (2016). Gender differences in technology usage—a literature review. Open Journal of Business and Management, 4, 51–59.CrossRefGoogle Scholar
  44. Grigoreanu, V., Beckwith, L., Fern, X., Yang, S., Komireddy, C., Narayanan, V., Cook, C., & Burnett, M.M. (2006). Gender differences in end-user debugging, revisited: what the miners found. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing. pp. 19–26.Google Scholar
  45. Grigoreanu, V., Cao, J., Kulesza, T., Bogart, C., Rector, K., Burnett, M. & Wiedenbeck, S. (2008). Can feature design reduce the gender gap in end-user software development environments? In Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing (VLHCC '08). IEEE computer society, Washington, DC, USA, 149–156.Google Scholar
  46. Grigoreanu, V., Burnett, M., Wiedenbeck, S., Cao, J., Rector, K., & Kwan, I. (2012). End user debugging strategies: a sense making perspective. Transactions on Computer-Human Interaction, 19(1), 5.CrossRefGoogle Scholar
  47. Harshbarger, N.L., & Rosson, M.B. (2012). wProjects: data-centric web development for female nonprogrammers. Proceedings of 2012 I.E. Symposium on Visual Languages and Human-Centric Computing, pp. 67–70.Google Scholar
  48. Hartzel, K. (2003). How self-efficacy and gender issues affect software adoption and use. Communications of the ACM, 46(9), 167–171.CrossRefGoogle Scholar
  49. Hoxmeier, J. A., Nie, W., & Purvis, G. T. (2000). The impact of gender and experience on user confidence in electronic mail. Journal of organizational and end user computing (JOEUC), 12(4), 10.Google Scholar
  50. Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91–112.CrossRefGoogle Scholar
  51. Hubona G.S., and Shirah G.W. (2004). The gender factor performing visualization tasks on computer media. In Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 4 - Volume 4 (HICSS '04), Vol. 4. IEEE Computer Society, Washington, DC, USA, 40097.3.Google Scholar
  52. Hung, Y.H., Wang, Y.S. & Chou, S.C.T. (2007). User acceptance of e-government services, Pacific Asia conference on information systems, Natl sun Yat-Sen university, Kaohsiung, Auckland, 4–6 July.Google Scholar
  53. Jason, B., Calitz, A., Greyling, J. (2010). The evaluation of an adaptive user interface model. In Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT ‘10). ACM, New York, NY, USA, 132–143.Google Scholar
  54. Johnson, R. D., Li, Y., & Dulebohn, H. J. (2016). Unsuccessful performance and future computer self-efficacy estimations: attributions and generalization to other software applications. Journal of Organizational and End User Computing (JOEUC), 28(1 (January 2016), 1–14.CrossRefGoogle Scholar
  55. Kim, Y. M. (2010). Gender role and the use of university library website resources: A social cognitive theory perspective. Journal of Information Science, 36(5), 603–617.Google Scholar
  56. Kim, J. K., & Ritter, F. E. (2015). Learning, forgetting, and relearning for keystroke- and mouse-driven tasks: relearning is important. Human–Computer Interaction, 30(1), 1–33.CrossRefGoogle Scholar
  57. Kissinger, C., Burnett, M., Stumpf, S., Subrahmaniyan, N., Beckwith, L., Yang, S., Rosson, M., (2006). Supporting end user debugging: What do users want to know? Advanced Visual Interfaces, ACM, pp. 135–142.Google Scholar
  58. Ko, A. J., Myers, B. A. (2004). Designing the Whyline: A Debugging Interface for Asking Questions About Program Failures. CHI 2004, Vienna, Austria, April 24–29, 151–158.Google Scholar
  59. Kulesza, T., Wong, W., Stumpf, S., Perona, S., White, R., Burnett, M.M., Oberst, I., and Ko, A.J. (2009) Fixing the program my computer learned: barriers for end users, challenges for the machine. In Proceedings of the 14th international conference on intelligent user interfaces (IUI '09) (pp. 187–196) ACM: New York.Google Scholar
  60. Kulesza, T., Stumpf, S., Wong, W., Burnett, M., Perona, S., Ko, A., & Oberst, I. (2011). Why-oriented end user debugging of naïve Bayes text classification. ACM trans. Interact. Intell. Syst. 1, 1, Article 2 (October 2011), 31 pages.Google Scholar
  61. Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423–1438.CrossRefGoogle Scholar
  62. Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: past, present, and future. Communications of the Association for Information System, 12(1), 752–780.Google Scholar
  63. Lieberman, H., Paternò, F., & Wulf, V. (2006). End User Development: an emerging paradigm. End User Development, 9, 1–8.CrossRefGoogle Scholar
  64. Lin, J., Wong, J., Nichols, J., Cypher, A. & Lau, T.A. (2010). End-user programming of mashups with vegemite. In Proceedings of the 13th international conference on intelligent user interfaces (IUI '09), pp. 97–106, February 2009.Google Scholar
  65. Liu, S.H., Liao, H.L., Chung Yuan, C.J. (2005). Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behaviour. Issues in Information Systems, 6(2), 95–104.Google Scholar
  66. Liu, G., Huang, S.-P. & Zhu, X.-K. (2008). User acceptance of internet banking in an uncertainand risky environment. The 2008 international conference on Risk Management & Engineering Management, Beijing, 4–6 November.Google Scholar
  67. Loewenstein, G. (1994). The psychology of curiosity: a review and reinterpretation. Psychology Bulletin, 116(1), 75–98.CrossRefGoogle Scholar
  68. Macías, J. A., & Paternò, F. (2008). Customization of web applications through an intelligent environment exploiting logical interface descriptions. Interacting with Computers, 20(1), 29–47.CrossRefGoogle Scholar
  69. Margolis, J., & Fisher, A. (2003). Unlocking the clubhouse. Cambridge: MIT Press.zbMATHGoogle Scholar
  70. Martinson, A. M. (2005). Playing with technology: Designing gender sensitive games to close the gender gap. In Working Paper SLISWP-03-05. School of Library and Information Science: Indiana University.Google Scholar
  71. Miller, R.C., Bolin, M.L., Chilton, B., Little, G., Webber, M. & Chen-Hsiang, Y. (2010). Rewriting the web with chicken foot. In No code required: giving users tools to transform the web (pp. 39–62) Burlington: Elsevier.Google Scholar
  72. Moolla, A., & Bisschoff, C. (2012). Validating a model to measure the brand loyalty of fast moving consumer goods. Journal of Social Sciences, 31(2), 101–115.CrossRefGoogle Scholar
  73. Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context. Information Management, 38(4), 217–230.CrossRefGoogle Scholar
  74. Moss, G. A., & Gunn, R. W. (2009). Gender differences in website production and preference aesthetics: preliminary implications for ICT in education and beyond. Behaviour & Information Technology, 28(5), 447–460.CrossRefGoogle Scholar
  75. Nestler, T., Namoun, A. & Schill, A. (2011). End-user development of service-based interactive web applications at the presentation layer. In Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS '11), June 2011, pp. 197–206.Google Scholar
  76. Nichols J., & Lau, T. (2008). Mobilization by demonstration: Using traces to re-author existing web sites. In Proceedings of the 13th international conference on intelligent user interfaces (IUI '08), pp. 149–158.Google Scholar
  77. Nunnally, J. (1967). Psychometric theory. New York: McGraw.Google Scholar
  78. Ong, C., & Lai, J. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behaviour, 22(5), 816–829.CrossRefGoogle Scholar
  79. Padilla-Melendez, A., Garrido-Moreno, A., & Del Aguila-Obra, A. R. (2008). Factors affecting e-collaboration technology use among management students. Computers & Education, 51(2), 609–623.CrossRefGoogle Scholar
  80. Padilla-Meléndez, A., del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63(April 2013), 306–317.CrossRefGoogle Scholar
  81. Park, S. (2009). An analysis of the technology acceptance model in understanding university students’ Behavioural intention to use e-learning. Education Technology & Society, 12(3), 150–162.MathSciNetGoogle Scholar
  82. Paternò, F. (2013). End user development: survey of an emerging field for empowering people. ISRN Software Engineering. vol. 2013, Article ID 532659, 11 pages.Google Scholar
  83. Protogeros, N., & Tzafilkou, K. (2015). Simple-talking database development: Let the end-user design a relational schema by using simple words. Computers in Human Behaviour, 48(July 2015), 273–289.Google Scholar
  84. Repenning, A., & Ioannidou, A. (2006). What makes end-user development tick? 13 Design guidelines. In H. Lieberman, F. Paternò, & V. Wulf (Eds.), End-user development: empowering people to flexibly employ advanced information and communication technology. Dordrecht: Kluwer.Google Scholar
  85. Rode, J. A. (2008). An ethnographic examination of the relationship of gender and end-user programming, Ph.D. Thesis, University of California Irvine.Google Scholar
  86. Rode, J., Rosson, M.B., & Pérez-Quiñones, J. (2006). End user development of web applications. End User Development , Human-Computer Interaction Series Volume 9, 2006, pp 161–182.Google Scholar
  87. Rosson, M.B., Sinha, H., Bhattacharya, M., & Zhao, D. (2007). Design planning in end-user web development. In Proceedings of 2007 I.E. Symposium on Visual Languages and Human-Centric Computing, pp. 189–196.Google Scholar
  88. Rosson, M.B., Sinha, H., & Edor, T. (2010). Design planning in end-user web development: gender, feature exploration and feelings of success. In Proceedings of 2010 I.E. Symposium on Visual Languages and Human-Centric Computing, pp. 141–148.Google Scholar
  89. Saadé, G., Nebebe, F., & Tan, W. (2007). Viability of the technology acceptance model in multimedia learning environments: a comparative study. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 175–184.Google Scholar
  90. Saadé, R. G., Kira, D., & Otrakji, C. A. (2012). Gender differences in interface type task analysis. International Journal of Information Systems and Social Change, 3(2), 1–23.CrossRefGoogle Scholar
  91. Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the internet: a study among undergraduates in Unimas. Educational Technology & Society, 8(4), 205–219.Google Scholar
  92. Scaffidi, C. C., Bogart, M. M., Burnett, A., Cypher, B., & Myers, M. S. (2010). Using traits of web macro scripts to predict reuse. Journal of Visual Languages & Computing, 21(5), 277–291.CrossRefGoogle Scholar
  93. Seifert, J., Pfleging, B., Bahamóndez, E., Hermes, M., Rukzio, E. and Schmidt, A. (2011). Mobidev: a tool for creating apps on mobile phones. In proceedings of the 13th international conference on human computer interaction with mobile devices and services (MobileHCI '11), pp. 109–112, ACM.Google Scholar
  94. Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3–4), 591–611.MathSciNetCrossRefzbMATHGoogle Scholar
  95. Soriano, J., Lizcano, D., Canas, M. A, Reyes, M. & Hierro, J.J. (2007). Fostering innovation in a mashup-oriented enterprise 2.0 Collaboration environment. In Proceedings of the SIWN international conference on adaptive business systems (ICABS '07), pp. 62–669, Chengdu, China.Google Scholar
  96. Spahn, M., Dörner, C., & Wulf, V. (2008). End user development: approaches towards a flexible software design questions to answer. ECIS 2008, 9th – 11th June 2008, Galway, Ireland.Google Scholar
  97. Stipek, D., & Gralinski, J. H. (1991). Gender differences in children’s achievement-related beliefs and emotional responses to success and failure in mathematics. Journal of Educational Psychology, 83(3).Google Scholar
  98. Subrahmaniyan, N., Beckwith, L., Grigoreanu, V., Burnett, M., Wiedenbeck, S., Narayanan, V., Bucht, K., Drummond, R., Fern, X. (2008). Testing vs. code inspection vs. what else? Male and female end users’ debugging strategies. In ACM conference on human factors in computing systems (pp. 617–626). New York: ACM.Google Scholar
  99. Sumak, B., Polancic, G. & Hericko, M. (2010). An empirical study of virtual learning environment adoption using UTAUT. Second international conference on mobile, hybrid, and on-line learning, pp. 17–22.Google Scholar
  100. Sundar, S. S., Bellur, S., Oh, J., Xu, Q., & Jia, H. (2014). User experience of on-screen interaction techniques: an experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping. Human–Computer Interaction, 29(2), 109–152.CrossRefGoogle Scholar
  101. Teo, T. (2011). Factors influencing teachers’ intention to use technology: model development and test. Computers & Education, 57(4), 1–25.CrossRefGoogle Scholar
  102. Teo, T. S. H., & Lim, V. K. G. (2000). Gender differences in internet usage and task preferences. Behaviour & Information Technology, 19(4), 283–295.CrossRefGoogle Scholar
  103. Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044.CrossRefGoogle Scholar
  104. Terzis, V., & Economides, A. A. (2012). Computer based assessment: gender differences in perceptions and acceptance. Computers in Human Behaviour, 27(6, November 2011), 2108–2122.CrossRefGoogle Scholar
  105. Tzafilkou, K., Protogeros, N., Charagiannidis, C., & Koumpis, A. (2016). Gender-based behavioral analysis for end-user development and the ‘RULES’ attributes. Education and Information Technologies, 22, 1–42.Google Scholar
  106. Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852.CrossRefGoogle Scholar
  107. Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use: perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education, 51(4), 1392–1404.CrossRefGoogle Scholar
  108. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived-ease of use: development and test. Decision Sciences, 27, 451–481.CrossRefGoogle Scholar
  109. Venkatesh, V., & Morris, M. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behaviour. MIS Quarterly, 24(1), 115–139.CrossRefGoogle Scholar
  110. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425–478.CrossRefGoogle Scholar
  111. Venkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3), 483–502.CrossRefGoogle Scholar
  112. Wang, Y. S., & Shih, Y. W. (2009). Why do people use information kiosks? A validation of theunified theory of acceptance and use of technology. Government Information Quarterly, 26(1), 158–165.CrossRefGoogle Scholar
  113. Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118.CrossRefGoogle Scholar
  114. Wang, H.-Y., Chan, T.-J., Huang, Y.-F., Wang, N.-C., & Chang, Y.-S. (2010). “Research hypotheses for gender activities in mobile internet”, IWCMC’10, Caen, 28 June-2 July.Google Scholar
  115. Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management, 28(3), 443–488.CrossRefGoogle Scholar
  116. Wong, K.-T., Teo, T., & Russo, S. (2012). Influence of gender and computer teaching efficacy on computer acceptance among Malaysian student teachers: An extended technology acceptance model. Australasian Journal of Educational Technology, 28(7), 1190–1207.CrossRefGoogle Scholar
  117. Wu, Y. L., Tao, Y. H., & Yang, P. C. (2007). Using UTAUT to explore the behavior of 3G mobile communication users. In M. Helander, M. Xie, M. Jaio, & K. C. Tan (Eds.), International conference on industrial engineering and engineering management (Vol. 1, pp. 199–203). Singapore: IEEE.Google Scholar
  118. Wu, Y.-L., Tao, W.-H., & Yang, P.-C. (2008). The use of unified theory of acceptance and use of technology to confer the behavioural model of 3G mobile telecommunication users. Journal of Statistics & Management Systems, 11(5), 919–949.CrossRefzbMATHGoogle Scholar
  119. Yurdugül, H. (2008). Minimum sample size for Cronbach’s coefficient alpha: a Monte-Carlo study. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 35(35), 397–405.Google Scholar
  120. Zbick, J., Jansen, M., & Milrad, M. (2014). Towards a web-based framework to support end-user programming of mobile learning activities. In 2014 I.E. 14th International Conference on Advanced Learning Technologies (ICALT), pp. 204–208. IEEE Press IEEE International Conference on Advanced Learning Technologies.Google Scholar
  121. Zhang, J., Huang, J., & Chen, J. (2010). Empirical research on user acceptance of mobile searches. Tsinghua Science & Technology, 15(2), 235–245.CrossRefGoogle Scholar
  122. Zhou, T. (2008). Exploring mobile user acceptance based on UTAUT and contextual offering. International symposium on electronic commerce and security, pp. 241–245.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Postgraduate Program in Information SystemsUniversity of MacedoniaThessalonikiGreece
  2. 2.Department of Accounting and FinanceUniversity of MacedoniaThessalonikiGreece

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