Article

ROBOTS IN EDUCATION: A JORDANIAN UNIVERSITY CASE STUDY🔗

ROBOTS EN EDUCACIÓN: EL CASO DE UNA UNIVERSIDAD DE JORDANIA🔗

English Full Article Citation
 

Author(s)🔗

Ala Almahameed , Universitat Rovira i Virgili, Spain
Dana AlShwayat , University of Petra, Jordan
Mario Arias-Oliva , Universitat Rovira i Virgili, Spain
Jorge Pelegrín-Borondo , Universidad de la Rioja, Spain

https://doi.org/10.35564/jmbe.2020.0011

Abstract🔗

This paper adopts a technology acceptance model used for studying Robot’s acceptance and focuses on the acceptance of robotic technologies. Despite a wide range of studies on the acceptance and usage of robotics technologies in different fields, there is lacuna of empirical evidence on the acceptance of robotics technologies in the educational context. We contribute to the scholarship on robotics technologies in an educational context, by using qualitative semi-structured interviews, and proposing a research model to empirically explore the main factors affecting the acceptance of robotics technologies, and particularly among university students. We contribute to practice by offering insights on users' expectations and intentions toward the potential use of robot services to both robot developers, and educational institutions alike. The results revealed a potential impact of effort expectancy, performance expectancy, social influence, and facilitating conditions on the intention behavior towards using robots as academic advisors. Additionally, an emergent dimension (i.e. emotions) was found to have an influence on the behavioral intentions, via its proposed impact on performance and effort expectancies. Overall, social characteristics of robots ought to be considered when investigating their acceptance, specifically when used as social entities in a human environment.

Keywords
robot, technology acceptance, robotics technologies, educational context

Resumen🔗

Este trabajo adopta un modelo de aceptación de tecnología utilizado para estudiar la aceptación de los Robots y enfocándose en la aceptación de las tecnologías robóticas. A pesar de una amplia gama de estudios sobre la aceptación y el uso de tecnologías robóticas en diferentes campos, existe una laguna de evidencia empírica sobre la aceptación de tecnologías robóticas en el contexto educativo. Contribuimos a la investigación sobre tecnologías de robótica en un contexto educativo, en particular en un contexto universitario, mediante el uso de entrevistas cualitativas semiestructuradas y proponiendo un modelo de investigación para explorar empíricamente los principales factores que afectan la aceptación de las tecnologías de robótica, y particularmente entre los estudiantes universitarios. Contribuimos a la práctica ofreciendo ideas sobre las expectativas e intenciones de los usuarios hacia el uso potencial de los servicios de robots tanto para los desarrolladores de robots como para las instituciones educativas por igual. Los resultados revelaron un impacto potencial de la expectativa de esfuerzo, la expectativa de rendimiento, la influencia social y las condiciones facilitadoras en el comportamiento intencional hacia el uso de robots como asesores académicos. Además, se descubrió que una dimensión emergente (i.e. las emociones) influye en las intenciones de comportamiento, a través de su impacto en el rendimiento y las expectativas de esfuerzo. En general, las características sociales de los robots deben considerarse al investigar su aceptación, específicamente cuando se usan como entidades sociales en un entorno humano.

Palabras clave
robot, aceptación tecnológica, tecnologías robóticas, contexto educativo

Received
5 June 2020

Accepted
21 June 2020

Copyright
This is an open access article under the CC BY-NC license.

Citation🔗

Almahameed, A.; AlShwayat, D.; Arias-Oliva, M. & Pelegrín-Borondo, J. (2020). Robots in education: a Jordanian university case study. Journal of Management and Business Education, x(x), x-x. https://doi.org/10.35564/jmbe.2020.0011

References🔗

Alaiad, A., & Zhou, L. (2013). Patients’ Behavioral Intention Toward Using Healthcare Robots. In Proceedings of the Nineteenth Americas Conference on Information Systems. Chicago, Illinois. Retrieved from http://aisel.aisnet.org/amcis2013/HealthInformation/GeneralPresentations/12/

Alaiad, A., & Zhou, L. (2014). The Determinants of Home Healthcare Robots Adoption: An Empirical Investigation. International Journal of Medical Informatics, 83(11), 825–840. https://doi.org/10.1016/j.ijmedinf.2014.07.003

Alaiad, A., Zhou, L., & Koru, G. (2013). An Empirical Study of Home Healthcare Robots Adoption Using the UTUAT Model. In Transactions of the International Conference on Health Information Technology Advancement 2013 (Vol. 2, pp. 185–198). Michigan, USA. Retrieved from https://scholarworks.wmich.edu/ichita_transactions/27/

Andreu, J. P., Deligianni, F., Ravi, D., & Yang, G.-Z. (2017). Artificial Intelligence and Robotics. arXiv. UK-RAS Network. https://doi.org/10.13140/RG.2.2.20572.6976

Anselm, S., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory. Thousand Oaks, California: Saga Publication.

Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics, 1(1), 71–81. https://doi.org/10.1007/s12369-008-0001-3

Bell, E., Bryman, A., & Harley, B. (2018). Business Research Methods. Glasgow: Bell & Bain Ltd (5th Editio). Oxford, England: Oxford University Press.

Bennewitz, M. (2004). Mobile robot navigation in dynamic environments using omnidirectional stereo. PhD Disseration. Albert Ludwigs University of Freiburg. Retrieved from https://freidok.uni-freiburg.de/data/1362

Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5), 809–826. https://doi.org/10.14742/ajet.818

Cheng, Y.-W., Sun, P.-C., & Chen, N.-S. (2018). The Essential Applications of Educational Robot: Requirement Analysis from the Perspectives of Experts, Researchers and Instructors. Computers and Education, 126, 399–416. https://doi.org/10.1016/j.compedu.2018.07.020

Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education, 63, 160–175. https://doi.org/10.1016/j.compedu.2012.12.003

Clark, S. M., Gioia, D. A., Ketchen Jr, D. J., & Thomas, J. B. (2010). Transitional identity as a facilitator of organizational identity change during a merger. Administrative Science Quarterly, 55(3), 397–438. https://doi.org/10.2189/asqu.2010.55.3.397

Conti, D., Di Nuovo, S., Buono, S., & Di Nuovo, A. (2015). A Cross-Cultural Study of Acceptance and Use of Robotics by Future Psychology Practitioners. In Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication (pp. 555–560). Kobe, Japan. https://doi.org/10.1109/ROMAN.2015.7333601

Corbin, J., & Strauss, A. (2014). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (4th Editio). Sage publications.
Corley, K. G. (2004). Defined by our strategy or our culture? Hierarchical differences in perceptions of organizational identity and change. Human Relations, 57(9), 1145–1177. https://doi.org/10.1177/0018726704047141

Corley, K. G., & Gioia, D. A. (2004). Identity ambiguity and change in the wake of a corporate spin-off. Administrative Science Quarterly, 49(2), 173–208. https://doi.org/10.2307/4131471

Crane, A. (2010). The dynamics of marketing ethical products: a cultural perspective. Journal of Marketing Management, 13(6), 561–577. https://doi.org/10.1080/0267257X.1997.9964493

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation. Massachusetts Institute of Technology.

Diana, M., & Marescaux, J. (2015). Robotic surgery. British Journal of Surgery, 102(2), e15–e28. https://doi.org/10.1002/bjs.9711

Escobar-Rodriguez, T., & Monge-Lozano, P. (2012). The acceptance of Moodle technology by business administration students. Computers and Education, 58(4), 1085–1093. https://doi.org/10.1016/j.compedu.2011.11.012

Fridin, M., & Belokopytov, M. (2014). Acceptance of socially assistive humanoid robot by preschool and elementary school teachers. Computers in Human Behavior, 33, 23–31. https://doi.org/10.1016/j.chb.2013.12.016

Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31. https://doi.org/10.1177/1094428112452151

Goodrich, M. A. (2008). Human–Robot Interaction: A Survey. Foundations and Trends® in Human–Computer Interaction, 1(3), 203–275. https://doi.org/10.1561/1100000005

Graaf, M. M. A. de, & Allouch, S. Ben. (2013). Exploring Influencing Variables for the Acceptance of Social Robots. Robotics and Autonomous Systems, 61, 1476–1486. https://doi.org/10.1016/j.robot.2013.07.007

Graaf, M. M. A. de, Allouch, S. Ben, & Dijk, J. A. G. M. Van. (2016). Long-term evaluation of a social robot in real homes. Interaction Studies, 17(3), 1–26. https://doi.org/10.1075/is.17.3.08deg

Graaf, M. M. A. de, Allouch, S. Ben, & van Dijk, J. A. G. M. (2019). Why Would I Use This in My Home? A Model of Domestic Social Robot Acceptance. Human-Computer Interaction, 34(2), 115–173. https://doi.org/10.1080/07370024.2017.1312406

Graetz, G., & Michaels, G. (2015). Robots at Work (No. 1335). CEP Discussion Paper. London: Centre for Economic Performance. Retrieved from https://ssrn.com/abstract=2575781

Groom, V., Nass, C., Chen, T., Nielsen, A., Scarborough, J. K., & Robles, E. (2009). Evaluating the effects of behavioral realism in embodied agents. International Journal of Human Computer Studies, 67(10), 842–849. https://doi.org/10.1016/j.ijhcs.2009.07.001

Haidegger, T., Sandor, J., & Benyo, Z. (2011). Surgery in space: The future of robotic telesurgery. Surgical Endoscopy, 25(3), 681–690. https://doi.org/10.1007/s00464-010-1243-3

Heerink, M. (2010). Assessing Acceptance of Assistive Social Robots by Aging Adults. PhD Thesis. University of Applied Sciences (HvA). https://doi.org/10.1007/s12369-010-1889/6

Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2009a). Measuring Acceptance of an Assistive Social Robot: A Suggested Toolkit. In IEEE International Workshop on Robot and Human Interactive Communication (pp. 528–533). Toyama, Japan: IEEE. https://doi.org/10.1109/ROMAN.2009.5326320

Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2009b). Measuring the Influence of Social Abilities on Acceptance of an Interface Robot and a Screen Agent by Elderly Users. In 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology (pp. 430–439). Cambridge, UK: British Computer Society. https://doi.org/10.1145/1671011.1671067

Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010a). Assessing Acceptance of Assistive Social Agent Technology by Older Adults: the Almere Model. International Journal of Social Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5

Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010b). Relating Conversational Expressiveness to Social Presence and Acceptance of an Assistive Social Robot. Virtual Reality, 14(1), 77–84. https://doi.org/10.1007/s10055-009-0142-1

Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating Factors Influencing the Physicians’ Adoption of Electronic Health Record (EHR) in Healthcare System of Bangladesh: An Empirical Study. International Journal of Information Management, 44, 76–87. https://doi.org/10.1016/j.ijinfomgt.2018.09.016

Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., & Hagita, N. (2010). A Communication Robot in a Shopping Mall. IEEE Transactions on Robotics, 26(5), 897–913. https://doi.org/10.1109/TRO.2010.2062550

Klamer, T., & Allouch, S. Ben. (2010). Acceptance and Use of a Social Robot by Elderly Users in a Domestic Environment. In 4th International Conference on Pervasive Computing Technologies for Healthcare. Munchen: IEEE. https://doi.org/10.4108/ICST.PERVASIVEHEALTH2010.8892

Lee, K. M., & Nass, C. (2003). Designing Social Presence of Social Actors in Human Computer Interaction. In Proceedings of the conference on Human factors in computing systems - CHI ’03 (pp. 289–296). Florida, United States: ACM. https://doi.org/10.1145/642611.642662

Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90(May 2018), 153–169. https://doi.org/10.1016/j.chb.2018.08.056
Mori, M. (1970). The Uncanny Valley. Energy, 7(4), 33–35.

Mucchiani, C., Sharma, S., Johnson, M., Sefcik, J., Vivio, N., Huang, J., … Yim, M. (2017). Evaluating older adults’ interaction with a mobile assistive robot. In IEEE International Conference on Intelligent Robots and Systems (pp. 840–847). Vancouver, Canada: IEEE. https://doi.org/10.1109/IROS.2017.8202246

Nag, R., & Gioia, D. A. (2012). From common to uncommon knowledge: Foundations of firm-specific use of knowledge as a resource. Academy of Management Journal, 55(2), 421–457. https://doi.org/10.5465/amj.2008.0352

Park, E., & Pobil, A. P. del. (2013). Users’ Attitudes Toward Service Robots in South Korea. Industrial Robot: An International Journal, 40(1), 77–87. https://doi.org/10.1108/01439911311294273

Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605. https://doi.org/10.1111/j.1467-8535.2011.01229.x

Pessaux, P., Diana, M., Soler, L., Piardi, T., Mutter, D., & Marescaux, J. (2015). Towards cybernetic surgery: robotic and augmented reality-assisted liver segmentectomy. Langenbeck’s Archives of Surgery, 400(3), 381–385. https://doi.org/10.1007/s00423-014-1256-9

Romportl, J. (2015). Beyond Artificial Intelligence: The Disappearing Human-Machine Divide. https://doi.org/10.1007/978-3-319-09668-1

Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640. https://doi.org/10.1016/j.chb.2010.06.011

Sharifi, M., Young, M. S., Chen, X., Clucas, D., & Pretty, C. (2016). Mechatronic design and development of a non-holonomic omnidirectional mobile robot for automation of primary production. Cogent Engineering, 3(1). https://doi.org/10.1080/23311916.2016.1250431

Sharkey, A. J. C. (2016). Should we welcome robot teachers? Ethics and Information Technology, 18(4), 283–297. https://doi.org/10.1007/s10676-016-9387-z

Shin, D.-H., & Choo, H. (2011). Modeling the Acceptance of Socially Interactive Robotics: Social Presence in Human–Robot Interaction. Interaction Studies, 12(3), 430–460. https://doi.org/10.1075/is.12.3.04shi

Shneier, M., & Bostelman, R. (2015). Literature Review of Mobile Robots for Manufacturing. NISTIR 8022. https://doi.org/10.6028/NIST.IR.8022

Shroff, R. H., Deneen, C. C., & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students ’ behavioural intention to use an e-portfolio system. Australasian Journal Of Educational Technology, 27(4), 600–618. Retrieved from http://hdl.handle.net/10722/160001%0AThis

Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46(4), 739–755. https://doi.org/10.1111/bjet.12169

Taylor, R. H., Menciassi, A., Fichtinger, G., Fiorini, P., & Dario, P. (2016). Medical Robotics and Computer-Integrated Surgery. In Springer Handbook of Robotics (pp. 1657–1684). https://doi.org/10.1007/978-3-319-32552-1_63

Timms, M. J. (2016). Letting Artificial Intelligence in Education out of the Box: Educational Cobots and Smart Classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701–712. https://doi.org/10.1007/s40593-016-0095-y

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. https://doi.org/10.2307/30036540

Wagner, K., Nimmermann, F., & Schramm-klein, H. (2019). Is It Human ? The Role of Anthropomorphism as a Driver for the Successful Acceptance of Digital Voice Assistants. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 1386–1395). Grand Wailea, Maui: HICSS. https://doi.org/10.24251/hicss.2019.169

Wu, X., & Bartram, L. (2018). Social Robots for People with Developmental Disabilities: A User Study on Design Features of a Graphical User Interface. Retrieved from http://arxiv.org/abs/1808.00121

Xu, B., Min, H., & Xiao, F. (2014). A brief overview of evolutionary developmental robotics. Industrial Robot, 41(6), 527–533. https://doi.org/10.1108/IR-04-2014-0324

Young, J. E. (2010). Exploring Social Interaction Between Robots and People. PhD Dissertation. THE UNIVERSIT Y OF CALGARY. Retrieved from https://dl.acm.org/citation.cfm?id=2049019