Skip to main content
SpringerLink
Log in

Gender-based behavioral analysis for end-user development and the ‘RULES’ attributes

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

This paper addresses the role of gender in End-User Development (EUD) environments and examines whether there are gender differences in performance and in correlations between performance and a set of behavioral attributes. Based on a review of the most prominent EUD-related behavioral Human Computer Interaction (HCI) theories, and the influence of gender on them, it attempts to classify all the gender related behavioral attributes influencing the end-users’ performance. Then, it theoretically selects a subset of these attributes, namely R isk-Perception, U sefulness-Perception, L earning Willingness, E ase-of-Use-Perception, and S elf-Efficacy, presents an example application and conducts a basic evaluation testing. The proposed attributes (their initials form the word RULES) can form the basis for the design of EUD-oriented user modeling techniques for gender-neutral self-adaptive software EUD environments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 8(2), 191–215.

    Article  Google Scholar 

  • Bandura, A. (1986). Social Foundations of Thought and Action: A social Cognitive Theory. Englewood Cliffs, N.J.: Prentice-Hall.

    Google Scholar 

  • Bandura, A. (1997). Self-efficacy: the exercise of control. New York7: W.H. Freeman & Co.

    Google Scholar 

  • Batrinca, L., Lepri, B., Mana, N., Pianesi, F., (2012). Multimodal recognition of personality traits in human-computer collaborative tasks. In Proceedings of the 14th ACM international conference on Multimodal interaction (ICMI ‘12). ACM, New York, NY, USA, 39–46.

  • Bauer, R. A. (1960). Consumer behavior as risk taking. In: R. S. Hancock (Ed.), Dynamic marketing for a changing world, Proceedings of the 43rd. Conference of the American Marketing Association, (pp. 389-398).

  • 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).

  • Beckwith, L., & Burnett, M. (2004). Gender: An Important Factor in End-User Programming Environments? IEEE Symposium on Visual Languages - Human Centric Computing, 2004, 107–114.

    Article  Google Scholar 

  • Beckwith, L., Burnett, M., (2007). Gender HCI Issues in End-User Software Engineering Environments. End user Software Engineering.

    Google Scholar 

  • Beckwith, L., Sorte, S. Burnett, M. Wiedenbeck, S. Chintakovid, T. and 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, USA, 153–160.

  • Beckwith, L., Burnett, M., Grigoreanu, V., & Wiedenbeck, S. (2006). In 2006 (Ed.), Gender HCI: What about the software? (pp. 83–87). IEEE (Nov: Computer.

    Google Scholar 

  • Beckwith, L., Inman, D., Rector, K., & Burnett, M. (2007). In 2007 (Ed.), On to the real world: Gender and self-efficacy in Exce (pp. 119–126). IEEE: In Proc. VLHCC.

    Google Scholar 

  • Beckwith, L., Cunha, J., Fernandes, J. P., & Saraiva, J. (2011). End-users productivity in model-based spreadsheets: an empirical study. In Proceedings of the Third international conference on End-user development (IS-EUD'11), Maria Francesca Costabile, Yvonne Dittrich, Gerhard Fischer, and Antonio Piccinno, Eds., pp. 282–288, Berlin, Heidelberg, Springer-Verlag.

  • Beyer, S., Rynes, K., Perrault, J., Hay, K., & Haller, S. (2003). Gender Differences in Computer Science Students (pp. 49–53). SIGCSE: Special Interest Group on Computer Science Education.

    Google Scholar 

  • Bickmore, T.W. and. Picard, R.W. (2005). Establishing and maintaining long-term human-computer relationships. ACM Trans. Comput.-Hum. Interact., 12:293–327.

  • Blackwell, A. (2002). First steps in programming: a rationale for attention investment models. In Proc. IEEE Human-Centric Computing Languages and Environments, 2–10.

  • Blackwell, A.F. and Hague, R. (2001). Designing a programming language for home automation. In: G. Kadoda (ed.), Proceedings of the 13th annual Workshop of the Psychology of Programming Interest Group (PPIG 2001). pp. 85–103.

  • Blackwell, A.F. and Morrison, C., (2010). A logical mind, not a programming mind: Psychology of a professional end-user. In Proceedings of the 22nd Annual Workshop of the Psychology of Programming Interest Group (PPIG 2010). September 19–22, 2010. Universidad Carlos III de Madrid, Leganès, Spain. Published by Maria Paloma Díaz Pérez and Mary Beth Rosson.

  • 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. Int. J. Human Comput. Stud., 67, 324–341.

    Article  Google Scholar 

  • Broos, A. (2005). Gender and information and communication technologies anxiety: Male self-assurance and female hesitation. Cyber Psychology and Behavior, 8(1), 11–32.

    Article  Google Scholar 

  • Burnett, M. (2009). What is end-user software engineering and why does it matter? End-User Development, 15–28.

  • Burnett, M., Cook, C., Pendse, O., Rothermel, G., Summet, J. and Wallace, C., (2003) End-user software engineering with assertions in the spreadsheet paradigm. In Proc. of International Conference on Software Engineering. 93–103.

  • 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, NY, USA, 21–24.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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. doi:10.1093/iwc/iwv046.

  • Byrnes, J., Miller, D., & Schafer, W. (1999). Gender differences in risk taking: a meta-analysis. Psychological Bulletin, 125(3), 367–383.

    Article  Google Scholar 

  • Carroll, J.M., (1998). The Nurnberg Funnel. MIT Press, (Ed.) Minimalism Beyond Cambridge, MA.

  • Carroll, J. M., & Rosson, M. B. (1987). Paradox of the active user. In J. M. Carroll (Ed.), Interfacing thought: cognitive aspects of human-computer interaction (pp. 80–111). Cambridge: MIT Press.

  • Chen, Y. H., & Corkindale, D. (2008). Towards an understanding of the behavioral intention to use online news services: an exploratory study. Internet Research, 18(3), 286–312.

    Article  Google Scholar 

  • ClickTale User Manual (2010) V0.5 www.clicktale.com

  • 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.

    Article  Google Scholar 

  • 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 Behavior, 17, 21–33.

    Google Scholar 

  • Costa Jr., P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) manual. Odessa, FL: Psychological Assessment Resources.

    Google Scholar 

  • Costa, P. T., Terracciano, A., & McCrae, R. R. (2001). Gender differences in personality traits across cultures: Robust and surprising findings. Journal of Personality and Social Psychology, 81(2), 322–331.

    Article  Google Scholar 

  • Costabile, M.F., Mussio, P., Provenza, L.P., and Piccinno, A., (2008). End users as unwitting software developers. In Proceedings of the 4th international workshop on End-user software engineering (WEUSE ‘08). ACM, New York, NY, USA, 6–10.

  • Cuccurullo, S., Francese, R., Risi, M. and Tortora, G. (2011). “MicroApps development on mobile phones.” End-User Development, M. Costabile, Y. Dittrich, G. Fischer, and A. Piccinno, Eds., vol. 6654 of Lecture Notes in Computer Science, pp. 289–294, Springer, Berlin, Germany.

  • Cyr, D., Hassanein, H., Head, M., & Ivanov, A. (2007). The role of social presence in establishing loyalty in eservice environments. Interacting with Computers, 19(1), 43–56.

    Article  Google Scholar 

  • Danado, J. and 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.

  • Danado, J. and 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.

  • Danado, J., Davies, M., Ricca, P. and Fensel, A. (2010) “An authoring tool for user generated mobile services.” in Proceedings of the 3rd Future Internet Conference on Future Internet (FIS ‘10), A. Berre, A. Gomez-Pérez, K. Tutschku, and D. Fensel, Eds., pp. 118–127, Springer.

  • Davies, D., Jindal-Snapeb, D., Collier, C., Digbya, R., Haya, P., Howea, A. (2012). Creative learning environments in education—A systematic literature review, Thinking Skills and Creativity 8, pp. 80–91, Elsevier, 2012

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.

    Article  Google Scholar 

  • Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research Note—How Does Personality Matter? Relating the Five-Factor Model to Technology Acceptance and Use. Information Systems Research., 19, 93–105.

    Google Scholar 

  • Eachus, P., & Cassidy, S. (2006). Academic journal article from Issues in Informing Science & Information Technology. Vol., 3.

  • Featherman, M., & Fuller, M. (2003). Applying TAM to eservices adoption: The moderating role of perceived risk. IEEE: In Proc. of Hawaii International Conference on System Sciences.

    Google Scholar 

  • Ferreira, S., Arroyo, E., Tarrago, R., & Blat, J. (2010). Applying Mouse Tracking to Investigate Patterns of Mouse Movements in Web Forms. Universitat Pompeu Fabra.

  • 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.

  • Ghiani, G., Paternò, F. and 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, Germany.

  • 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, Volume 87, March 2016, pp.s 38–64.

  • 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. 19–26.

  • 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 I.E. Symposium on Visual Languages and Human-Centric Computing (VLHCC ‘08). IEEE Computer Society, Washington, DC, USA, 149–156.

  • Harshbarger, N. L., & Rosson, M. B. (2012). wProjects: Data-centric Web Development for Female Nonprogrammers. Proceedings of 2012 IEEE Symposium on Visual Languages and Human-Centric Computing (pp. 67–70).

  • Hartzel, K., (2003). How self-efficacy and gender issues affect software adoption and use. Comm. ACM 46, 9. 167–171.

  • Hothi, J. & Hall, W. (1998). An evaluation of adapted hypermedia techniques using static user modeling. In Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia, Pittsburgh, PA, 45–50, June 1998.

  • Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K. (1998). Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. Proceedings of the fourteenth Conference on Uncertainty in AI.

  • Hubona G.S., 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.

  • Hui, B., & Boutilier, C. (2006). Who’s asking for help?: a bayesian approach to intelligent assistance. In IUI ’06: Proceedings of the 11th international conference on Intelligent user interfaces (pp. 186–193). New York: ACM Press.

  • 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.

  • Johnson, A., & Niels, T. (2005). User modeling, handbook of human factors in web design, Lawrence Erlbaum Associates (pp 424-439).

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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? (pp. 135–142). Advanced Visual Interfaces: ACM.

    Google Scholar 

  • 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.

  • Ko, A. J., Myers, B., Rosson, M. B., Rothermel, G., Shaw, M., Wiedenbeck, S., Abraham, R., et al. (2011). The state of the art in end-user software engineering. ACM Computing Surveys, 43(3), 1–44.

    Article  Google Scholar 

  • Kobsa, A. (2001). Generic user modeling systems. User Modeling and User-Adapted Interaction, 11(1-2), 49–63.

  • Kulenza, 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). ACM, New York, NY, USA, 187–196.

  • Lakiotaki, K., Matsatsinis, N. F., & Tsoukias, A. (2011). Multicriteria user modeling in recommender systems. IEEE Intelligent Systems, 26(2), 64–76.

  • Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423–1438.

  • Lee, G. and Chen, Z. (2007) Investigating the Differences in Web Browsing Behavior of Chinese and European User Using Mouse Tracking. N. Aykin (Ed.): Usability and Internationalization, Part I, HCII 2007, LNCS 4559, pp. 502–512, 2007. Springer-Verlag Berlin Heidelberg.

  • Lieberman, H., Paternò, F., & Wulf, V. (2006). End User Development : An emerging paradigm. End User Development, 1–8.

  • Lin, J., Wong, J., Nichols, J., Cypher, A. and 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

  • Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychology Bulletin, 116(1), 75–98.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Margolis, J., & Fisher, A. (2003). Unlocking the Clubhouse. MIT Press.

  • Marin, E. (2014). Experiential learning: empowering students to take control of their learning by engaging them in an interactive course simulation environment, The 6th International Conference Edu World 2014 “Education Facing Contemporary World Issues”, Procedia - Social and Behavioral Sciences 180, pp. 854–859, November 2014

  • Martins, A. C., Faria, L., Vaz de Carvalho, C., & Carrapatoso, E. (2008). User Modeling in Adaptive Hypermedia Educational Systems. Educational Technology & Society, 11(1), 194–207.

  • 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 

  • Mastor, K. A. (2003). Personality traits and gender differences in the selection of academic major among Malay students. Journal Pendidikan, 28, 3–13.

    Google Scholar 

  • McCrae, R. R., & Costa Jr., P. T. (1999). A five-factor theory of personality. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 139–153). New York: Guilford Press.

    Google Scholar 

  • McIlroy, D., Bunting, B., Tierney, K., Gordon, M., (2001). The relation of gender and background experience to self-reported computing anxieties and cognitions. Computers in Human Behavior. 17, 1, (1 January 2001), Pages 21–33.

  • McLeod, L., and MacDonell, S.G., (2011). Factors that affect software systems development project outcomes: A survey of research. ACM Comput. Surv. 43, 4, Article 24 (October 2011), 56 pages.

  • Mejía, A., Juárez-Ramírez, R., Inzunza, R., & Valenzuela, R. (2012). Implementing adaptive interfaces: a user model for the development of usability in interactive systems. In V. Potdar & D. Mukhopadhyay (Eds.), Proceedings of the CUBE International Information Technology Conference, 2 (pp. 598–604). New York: ACM.

  • 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, Mass, USA: Elsevier.

    Chapter  Google Scholar 

  • Moolla, A., & Bisschoff, C. (2012). Validating a Model to Measure the Brand Loyalty of Fast Moving Consumer Goods. J. SocSci, 31(2), 101–115.

    Google Scholar 

  • Moon, J., & Kim, Y. (2001). Extending the TAM for a world-wide-web context. Information and Management, 38(4), 217–230.

    Article  MathSciNet  Google Scholar 

  • Moss, G., & Gunn, R. (2009). Gender differences in website production andpreference aesthetics: preliminary implications for ICT in education and beyond. Behaviour & Information Technology 26(5), 447–460.

  • Nass, C., & Brave, S. (2005). Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship. MIT Press.

  • Nestler, T., Namoun, A. and 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.

  • Nichols J., and 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.

  • Nunes, M. A., Cerri S. A., Blanc N. (2008). Improving recommendations by using personality traits in user profiles. International Conferences on Knowledge Management and New Media Technology, Sep 2008, Graz, Austria (pp. 92-100).

  • Nunnally, J. (1967). Psychometric Theory. New York: McGraw.

    Google Scholar 

  • Ong, C., & Lai, J. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816–829.

    Article  Google Scholar 

  • Osvalder, A.-L., & Ulfvengren, P. (2009). Human-technology systems. In G. Bhgard et al. (Eds.), Work and technology on human terms. Sweden: Prevent.

    Google Scholar 

  • Özbek, V., Alnıaçık, Ü., Koc, M.F., Akkılıç, E., Kaş, E. (2014). The Impact of Personality on Technology Acceptance: A Study on Smart Phone Users, Procedia - Social and Behavioral Sciences, Volume 150, 15 September 2014.

    Google Scholar 

  • Papamitsiou, Z.; Economides, A.A. (2014). The Effect of Personality Traits on Students’ Performance during Computer-Based Testing: A Study of the Big Five Inventory with Temporal Learning Analytics, in Advanced Learning Technologies (ICALT), 2014 I.E. 14th International Conference on , vol., no., pp.378–382, 7–10 July 2014.

  • Paternò, F. (2013). “End User Development: Survey of an Emerging Field for Empowering People.” ISRN Software Engineering, vol. 2013, Article ID 532659, 11 pages

  • Protogeros, N., & Tzafilkou, K. (2015). Simple-talking database development: Let the end-user design a relational schema by using simple words, Computers in Human Behavior, Volume 48. July, 2015, 273–289.

    Google Scholar 

  • Rich, E. (1998). User modeling via stereotypes. In M. T. Maybury & W. Wahlster (Eds.), Readings in intelligent user interfaces (pp. 329–342). San Francisco: Morgan Kaufmann Publishers Inc..

  • Robertshon, T.J., Prabhakararao, S., Burnett, M., Cook, C., Ruthruff, J. R., Beckwith, L., Phalgune, A., (2004). Impact of interruption style on end-user debugging. In Proceedings of the ACM Conference on Human Factors in Computing Systems. 287–294.

  • Rode, J.A. (2008). An ethnographic examination of the relationship of gender & end-user programming, Ph.D. Thesis, University of California Irvine.

  • Rode, J., Rosson , M.B., (2003). Programming at Runtime: Requirements Paradigms for Nonprogrammer Web Application Development. IEEE HCC 2003.Auckland, New Zealand. Oct. 28–31.

  • Rode, J., Rosson, M. B., & Quiñones, M. A. P. (2005). End user development of web applications. End User Development. Human-Computer Interaction Series Volume, 9(2006), 161–182.

    Google Scholar 

  • Rosson, M. B., Ballin, J., & Nash, H. (2004). Everyday programming: Challenges and opportunities for informal web development. Visual Languages and Human-Centric Computing 2004 (pp. 123–130). New York: IEEE.

    Google Scholar 

  • Rosson, M. B., Sinha, H., Bhattacharya, M., Zhao, D. (2007). Design planning in end-user web development. In Proceedings of 2007 IEEE symposium on visual languages and human-centric computing (pp. 189–196).

  • 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 IEEE symposium on visual languages and human-centric computing (pp. 141–148).

  • Ruthruff, J.R., Phalgune, A., Beckwith, L., Burnett, M., Cook, C., (2004). Rewarding “good” behavior: End-user debugging and rewards. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centered Computing. 115–122.

  • 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.

    Article  Google Scholar 

  • Saleem, H., Beaudry, A., Croteau, A.M., (2011). Antecedents of computer self-efficacy: A study of the role of personality traits and gender, Computers in Human Behavior, Volume 27, Issue 5, September 2011, Pages 1922–1936.

  • Scaffidi, C., Myers, B., Shaw, M. (2008). Topes: reusable abstractions for validating data. International Conference on Software Engineering (ICSE 2008), Leipzig, Germany.

  • Scaffidi, C.C., Bogart, M.M., Burnett, A., Cypher, B., Myersm, M.S. (2010). Using Traits of Web Macro Scripts to Predict Reuse. Journal of Visual Languages & Computing, vol. 21, issue 5, pp. 277–291.

  • 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

  • Shea, P., & Bidjerano, T. (2010). Learning presence: towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721–1731.

    Article  Google Scholar 

  • Shneiderman, B. (1980). Software Psychology: Human Factors in Computer and Information Systems. Winthrop Publishers.

  • Soriano, J., Lizcano, D., Canas, M. A., Reyes, M., Hierro, J. J. (2007). Fostering innovation in a mashuporiented enterprise 2.0 collaboration environment. In Proceedings of the SIWN International Conference on Adaptive Business Systems (ICABS '07) (pp. 62–669). Chengdu, China.

  • Sosnovsky, S., & Dicheva, D. (2010). Ontological technologies for user modelling. International Journal of Metadata, Semantics and Ontologies, 5(1), 32–71.

  • Srivastava, S., John, O. P., Gosling, S. D., & Potter, J. (2003). Development of personality in early and middle adulthood: Set like plaster or persistent change? Journal of Personality and Social Psychology, 84(5), 1041–1053.

    Article  Google Scholar 

  • Stipek, D., & Gralinski, J. H. (1991). Gender differences in children’s achievement-related beliefs and emotional responses to success and failure in mathematics. J. of Educational Psychology, 83(3).

  • 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 Proc. CHI, ACM, 617–626.

  • Sun, H., & Zhang, P. (2008). An exploration of affect factors and their role in user technology acceptance: mediation and causality. Journal of the American Society for Information Science and Technology, 59(8), 1–12.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology, 31(3), 235–251.

    Article  Google Scholar 

  • Terzis, V., Economides, A.A., (2011). Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 27, 6, November 2011, 2108–2122.

  • Terzis, V., Moridis, C.N., Economides, A.A., (2012). How student’s personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM, Computers in Human Behavior, Volume 28, Issue 5, September 2012, Pages 1985–1996, ISSN 0747–5632

  • Thagard, P. (2006). Hot Thought: Machanisms and Applications of Emotional Cognition. Cambridge, MA, USA: A Bradford Book- MIT Press.

    Google Scholar 

  • Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143.

    Article  Google Scholar 

  • Trappl, R., Payr, S., Petta, P., (2003). Emotions in Humans and Artifacts.MIT Press, Cambridge, MA, USA.

  • Trauth, E. M. (2002). Odd girl out: an individual differences perspective on women in the IT profession. Information Technology and People, 15(2), 98–118.

  • Trauth, E. M. (2006a). Encyclopedia of gender and information technology. Hershey: IGI Global.

  • Trauth, E. M. (2006b). An agenda for research on gender diversity in the global information economy. In E. M. Trauth (Ed.), The encyclopedia of gender and information technology (pp. xxix–xxiii). Hershey: Idea Group Publishing.

  • Trauth, E. M. (2006c). Theorizing gender and information technology research. In E. M. Trauth (Ed.), Encyclopedia of gender and information technology (pp. 1154–1159). Hershey: Idea Group Publishing.

  • Trauth, E. M. (2013). The role of theory in gender and information systems research. Information & Organization, 23(4), 277–293.

  • Trauth, E. M., Quesenberry, J. L., & Morgan, A. J. (2004). Understanding the under representation of women in IT: toward a theory of individual differences. Proceedings of the ACM SIGMIS Conference on Computer Personnel Research. (Tucson, AZ, April): 114-119.

  • Trauth, E. M., Quesenberry, J. L. & Huang, H. (2009). Factors influencing career choice for women in the global information technology workforce, in technological advancement in developed and developing countries: discoveries in global information management. G. Hunter and F. Tan, Eds., Hershey, PA: IGI Global

  • 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 behavior. MIS Quarterly, 24(1), 115–139.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  MathSciNet  Google Scholar 

  • Wilson, A., Burnett, M., Beckwith, L., Granatir, O., Casburn, L., Cook, C., Durham, M., & Rothermel, G. (2003). Harnessing Curiosity to Increase Correctness in End-User Programming. In ACM Conference on Human Factors in Computing Systems. New: ACM.

    Google Scholar 

  • Zang, N., & Rosson, M. B. (2010). Mashups for Web-active end users. In A. Cypher, M. Dontcheva, T. Lau, & J. Nichols (Eds.), No Code Required: Giving Users Tools to Transform the Web (pp. 409–423). Morgan Kaufmann: San Francisco.

    Google Scholar 

  • Zbick, J., Jansen, M., and 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.

  • Zhang, H., Song, Y., & Song, H. -T. (2007). Construction of ontology-based user model for web personalization. In C. Conati, K. Mccoy, & G. Paliouras (Eds.), Proceedings of the 11th international conference on User Modeling (UM '07) (pp. 67–76). Berlin, Heidelberg: Springer-Verlag.

Download references

Author information

Authors and Affiliations

  1. Interdepartmental Programme of Postgraduate Studies in Information Systems, University of Macedonia, Thessaloniki, Greece

    Katerina Tzafilkou

  2. Department of Accounting and Finance, University of Macedonia, Thessaloniki, Greece

    Nicolaos Protogeros

  3. Department of Special Education, University of Thessaly, Volos, Greece

    Charalampos Karagiannidis

  4. Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany

    Adamantios Koumpis

Authors
  1. Katerina Tzafilkou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolaos Protogeros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Charalampos Karagiannidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adamantios Koumpis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katerina Tzafilkou.

Appendix

Appendix

As Table 8 presents, there is internal validity of the rules constructs since all values of cronbach’s alpha are greater than 0,7 revealing high level of internal consistency

Table 8 Validity of the measurement model
Full size table
Table 9 Test of normality
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tzafilkou, K., Protogeros, N., Karagiannidis, C. et al. Gender-based behavioral analysis for end-user development and the ‘RULES’ attributes. Educ Inf Technol 22, 1853–1894 (2017). https://doi.org/10.1007/s10639-016-9519-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-016-9519-4

Keywords

Search

Navigation