“Use” as a Conscious Thought: Towards a Theory of “Use” in Autonomous Things
DOI:
https://doi.org/10.3127/ajis.v28.4611Keywords:
autonomous things, conscious use, conscious thoughts, scale developmentAbstract
The way users perceive and use information systems artefacts has been mainly studied from the notion of behavioral beliefs, deliberate cognitive efforts, and physical actions performed by human actors to produce certain outcomes. The next generation of information systems, however, can sense, respond, and adapt to environments without necessitating similar cognitive efforts, physical contact, or explicit instructions to operate. Therefore, by leveraging theories of consciousness and technology use, this research aims to advance an alternative understanding of the "use" associated with the next generation of IS artefacts that do not require deliberate cognitive efforts, physical manipulation, or explicit instructions to yield outcomes. The theory and proposed model were refined and validated through the burst detection technique, IS expert involvement (n=10), a pilot study (n=130), and end-user surveys (n= 119). Structural equating modelling techniques were employed to test the theory. We show that unlike the manually operated IS artefacts, the “use” of a fully autonomous artefact is a conscious thought rather than a physical activity of operating a system to produce certain outcomes. We argue that, unlike the traditional notions of use associated with manually operated technologies, conscious use is not characterized solely by behavioral beliefs stemming from logical and reflective cognitive and physical efforts (e.g., effort expectancy). We propose the notion of conscious use within the context of fully autonomous entities and empirically validate its measure. Additionally, we offer recommendations for future research directions in this area. The conceptualization of this new theory for fully autonomous IS artefacts adds significant academic value to the literature given the convergence of AI-based machine learning systems and cognitive computing systems.
References
Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247. https://doi.org/10.2307/249577
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. https://doi.org/10.2307/3250951
Ahuja, M. K., & Thatcher, J. B. (2005). Moving beyond intentions and toward the theory of trying: Effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29(3), 427–459. https://doi.org/10.2307/25148691
Ajibade, P. (2018). Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Library Philosophy and Practice (e-journal), 1941. Retrieved from http://digitalcommons.unl.edu/libphilprac/1941
Albus, J., & Antsaklis, P. J. (1998). Panel discussion: Autonomy in engineering systems: What is it and why is it important? Setting the stage: Some autonomous thoughts on autonomy. In Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) Intell (pp. 520–521).
Andrade, A. D., & Doolin, B. (2016). Information and communication technology and the social inclusion of refugees. MIS Quarterly, 40(2), 405–416. Retrieved from https://www.jstor.org/stable/26628912
Arents, J., Abolins, V., Judvaitis, J., Vismanis, O., Oraby, A., & Ozols, K. (2021). Human–robot collaboration trends and safety aspects: A systematic review. Journal of Sensor and Actuator Networks, 10(3), 48. https://doi.org/10.3390/jsan10030048
Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. https://doi.org/10.1016/j.comnet.2010.05.010
Baars, B. (1988). A cognitive theory of consciousness. Cambridge, UK: Cambridge University Press.
Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Planning, 45(5–6), 359–394. https://doi.org/10.1016/j.lrp.2012.10.001
Benbasat, I., & Zmud, R. W. (2003). The identity crisis within the IS discipline: Defining and communicating the discipline’s core properties. MIS Quarterly, 27(2), 183–194. https://doi.org/10.2307/30036527
Bimbraw, K. (2015). Autonomous cars: Past, present and future - A review of the developments in the last century, the present scenario and the expected future of autonomous vehicle technology. In Proceedings of ICINCO 2015 - 12th International Conference on Informatics in Control, Automation and Robotics (pp. 191–198).
Bohrnstedt, G. W. (1970). Reliability and Validity Assessment in Attitude Measurement. In G. F. Summers (Ed.), Attitude Measurement. Skokie, IL: Rand McNally.
Borriello, G. (2000). The Challenges to Invisible Computing. IEEE Computer, Integrated Engineering column, 33(11), 123-125.
Boucha, D., Amiri, A., & Chogueur, D. (2017). Controlling electronic devices remotely by voice and brain waves. In 2017 International Conference on Mathematics and Information Technology (ICMIT) (pp. 38-42). Adrar. https://doi.org/10.1109/MATHIT.2017.8259693.
Broadbent, E. (2017). Interactions with robots: The truths we reveal about ourselves. Annual Review of Psychology, 68, 627-652. https://doi.org/10.1146/annurev-psych-010416-043958
Broder, H. L., McGrath, C., & Cisneros, G. J. (2007). Questionnaire development: Face validity and item impact testing of the Child Oral Health Impact Profile. Community Dentistry and Oral Epidemiology, 35, 8–19.
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.
Burton-Jones, A., & Straub, D. W. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246.
http://www.jstor.org/stable/23015887
Chuttur, M. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. In All Sprouts Content (p. 290).
https://aisel.aisnet.org/sprouts_all/290
Dautenhahn, K. (2007). Socially intelligent robots: dimensions of human-robot interaction. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1480), 679-704. https://doi.org/10.1098/rstb.2006.2004
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davenport, T. H. (2019). The AI advantage: How to put the artificial intelligence revolution to work. MIT Press. https://mitpress.mit.edu/9780262538008/the-ai-advantage/
Del Aguila-Obra, A. R., & Padilla-Meléndez, A. (2006). Organizational factors affecting Internet technology adoption. Internet Research, 16, 94-110. https://doi.org/10.1108/10662240610642569
De Melo, C. M., Marsella, S., & Gratch, J. (2019). Human cooperation when acting through autonomous machines. Proceedings of the National Academy of Sciences of the United States of America, 116(9), 3482–3487. https://doi.org/10.1073/pnas.1817656116
Delone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19, 9-30. https://doi.org/10.1080/07421222.2003.11045748
Demetis, D., & Lee, A. (2018). When Humans Using the IT Artifact Becomes IT Using the Human Artifact. Journal of the Association for Information Systems, 19, 929-952. https://doi.org/10.17705/1jais.00514
Dey, A. (2001). Understanding and Using Context. Personal and Ubiquitous Computing, 5, 4-7. https://doi.org/10.1007/s007790170019
Dhar, R., & Gorlin, M. (2013). A dual-system framework to understand preference construction processes in choice. Journal of Consumer Psychology, 23(4), 528–542. https://doi.org/10.1016/j.jcps.2013.02.002
de Guinea, A. O., & Markus, M. L. (2009). Why Break the Habit of a Lifetime? Rethinking the Roles of Intention, Habit, and Emotion in Continuing Information Technology Use. MIS Quarterly, 33(3), 433–444. https://doi.org/10.2307/20650303
Desai, M., Stubbs, K., Steinfeld, A., & Yanco, H. (2017). Creating trustworthy robots: Lessons and inspirations from automated systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 7(4), 1–24.
Dijksterhuis, A., & Nordgren, L. F. (2006). A theory of unconscious thought. Perspectives on Psychological Science, 1(2), 95–109. https://doi.org/10.1111/j.1745-6916.2006.00007.x
Dix, A. (2017). Activity modelling for low-intention interaction. In B. Weyers, J. Bowen, A. Dix, & P. Palanque (Eds.), The handbook of formal methods in human-computer interaction (pp. 183–210). Cham: Springer International Publishing.
Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64. https://doi.org/10.1518/001872095779049543
Endsley, M. R. (1999). Level of automation effects on performance, situation awareness and workload in a dynamic control task. Ergonomics, 42(3), 462–492.
Endsley, M. R. (2017). Autonomous driving systems: A preliminary naturalistic study of the Tesla Model S. Journal of Cognitive Engineering and Decision Making, 11(3), 225–238. https://doi.org/10.1177/1555343417695197
Eriksson, A., Petermeijer, S. M., Zimmermann, M., De Winter, J. C., Bengler, K. J., & Stanton, N. A. (2017). Rolling out the red (and green) carpet: Supporting driver decision making in automation-to-manual transitions. IEEE Transactions on Human-Machine Systems, 49(1), 20–31.
Ernst, C. (2020). Artificial intelligence and autonomy: Self-determination in the age of automated systems. In T. W. & T. Rademacher (Eds.), Regulating Artificial Intelligence (pp. 53–73). Cham, Switzerland: Springer International Publishing.
Feroz, A. K., Zo, H., Eom, J., & Chiravuri, A. (2023). Identifying organizations’ dynamic capabilities for sustainable digital transformation: A mixed methods study. Technology in Society, 73, 102257. https://doi.org/10.1016/j.techsoc.2023.102257
Gerlach, J. H., & Kuo, F.-Y. (1991). Understanding Human-Computer Interaction for Information Systems Design. MIS Quarterly, 15(4), 527–549. https://doi.org/10.2307/249456
Goodrich, M., & Schultz, A. (2007). Human-Robot Interaction: A Survey. Foundations and Trends in Human-Computer Interaction, 1, 203-275. http://doi.org/10.1561/1100000005
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). Pls-Sem: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152.
Hair, J. F., Risher Jeffrey, J., Sarstedt, M., & Ringle Christian, M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.
Henningsson, S., & Kettinger, W. J. (2016). Understanding information systems integration deficiencies in mergers and acquisitions: A configurational perspective. Journal of Management Information Systems, 33(4), 942–977.
https://doi.org/10.1080/07421222.2016.1267516
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Henley, S. H. A. (1984). Unconscious perception re-revisited: A comment on Merikle’s (1982) paper. Bulletin of the Psychonomic Society, 22, 121–124. https://doi.org/10.3758/BF03333780
Hewitt, C., Politis, I., Amanatidis, T., & Sarkar, A. (2019). Assessing public perception of self-driving cars: The autonomous vehicle acceptance model. In Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 518–527). Marina del Ray, California: Association for Computing Machinery.
Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967-988. https://doi.org/10.1016/0149-2063(95)90050-0
Hoffman, G. (2012). Embodied Cognition for Autonomous Interactive Robots. Topics in Cognitive Science, 4, 759-772. https://doi.org/10.1111/j.1756-8765.2012.01218.x
Hossain, M. A., Atrey, P. K., & Saddik, A. E. (2007). Smart mirror for ambient home environment. In IET Conference Proceedings (pp. 589–596). Institution of Engineering and Technology. DOI: 10.1049/cp:20070431
Im, I., Hong, S., & Kang, M. S. (2011). An international comparison of technology adoption: Testing the UTAUT model. Information & Management, 48, 1-8. https://doi.org/10.1016/j.im.2010.09.001
Inagaki, T., & Sheridan, T. (2019). A critique of the SAE conditional driving automation definition, and analyses of options for improvement. Cognition, Technology & Work, 21. https://doi.org/10.1007/s10111-018-0471-5
Ng, I., & Wakenshaw, S. (2016). The Internet-of-Things: Review and Research Directions. International Journal of Research in Marketing, 34.
https://doi.org/10.1016/j.ijresmar.2016.11.003
Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29(3), 525–557.
Jarvis, C., Burke, M., MacKenzie, S., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.
Jayaraman, S. K., Creech, C., Tilbury, D. M., Yang, X. J., Pradhan, A. K., Tsui, K. M., & Robert, L. P. (2019). Pedestrian trust in automated vehicles: Role of traffic signal and AV driving behavior. Frontiers in Robotics and AI, 6, 117.
Kabat-Zinn, J. (1994). Wherever you go, there you are: Mindfulness meditation in everyday life. New York, USA: Hyperion.
Kahneman, D. (2012). Thinking, Fast and Slow. London, United Kingdom: Penguin Books Ltd.
Khan, G. F., & Trier, M. (2019). Assessing the long-term fragmentation of information systems research with a longitudinal multi-network analysis. European Journal of Information Systems, 28(4), 370–393. https://doi.org/10.1080/0960085X.2018.1547853
Kim, S. S., Malhotra, N. K., & Narasimhan, S. (2005). Research note—Two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418–432.
Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397.
Kramer, A., Guillory, J., & Hancock, J. (2014). Experimental Evidence of Massive-Scale Emotional Contagion Through Social Networks. Proceedings of the National Academy of Sciences of the United States of America, 111. https://doi.org/10.1073/pnas.1320040111
Kumar, A., Metze, F., & Kam, M. (2014). Enabling the Rapid Development and Adoption of Speech-User Interfaces. Computer, 47(01), 40-47. https://doi.org/10.1109/MC.2014.11
Kwak, S. S., Kim, J. S., & Choi, J. J. (2017). The effects of organism- versus object-based robot design approaches on the consumer acceptance of domestic robots. International Journal of Social Robotics, 9(3), 359–377.
Lee, A., Thomas, M., & Baskerville, R. (2014). Going back to basics in design science: From the information technology artifact to the information systems artifact. Information Systems Journal, 25. https://doi.org/10.1111/isj.12054
Lee, Y., Kozar, K., & Larsen, K. (2003). The Technology Acceptance Model: Past, Present, and Future. Technology, 12. https://doi.org/10.17705/1CAIS.01250
Levy, Y., & Ellis, T. J. (2006). A systems approach to conduct an effective literature review in support of information systems research. International Journal of an Emerging Transdiscipline, 9.
Leydesdorff, L. (2006). The knowledge-based economy: Modeled, measured, simulated. Boca Raton, FL, USA: Universal-Publishers.
Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737.
MacDorman, K. F., & Entezari, S. O. (2015). Individual differences predict sensitivity to the uncanny valley. Interaction Studies, 16(2), 141–172.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.
Marchetti, G. (2018). Consciousness: A unique way of processing information. Cognitive Processing, 19(3), 435–464.
Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The autonomy paradox: The implications of mobile email devices for knowledge professionals. Organization Science, 24(5), 1337–1357.
Mettler, T., & Wulf, J. (2019). Physiolytics at the workplace: Affordances and constraints of wearables use from an employee's perspective. Information Systems Journal, 29(1), 245–273.
Mihaly, C. (1990). Flow: The psychology of optimal experience. New York, NY, USA: Harper and Row.
Moody, D. L. (2004). Cognitive load effects on end user understanding of conceptual models: An experimental analysis. In A. Benczúr, J. Demetrovics, & G. Gottlob (Eds.), Advances in Databases and Information Systems (pp. 129–143). Berlin, Heidelberg, Germany: Springer.
Murat Dikmen, & Catherine M. Burns. (2016). Autonomous driving in the real world: Experiences with Tesla Autopilot and Summon. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Automotive'UI 16) (pp. 225–228). Association for Computing Machinery.
Musk, E. (2019). Neuralink: An Integrated Brain-Machine Interface Platform With Thousands of Channels. Journal of Medical Internet Research, 21(10), e16194. https://www.jmir.org/2019/10/e16194 DOI: 10.2196/16194
Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling: A Multidisciplinary Journal, 10(4), 622–647.
Norman, D. A., Ortony, A., & Russell, D. M. (2003). Affect and machine design: Lessons for the development of autonomous machines. IBM Systems Journal, 42(1), 38–44.
Nomura, T., Kanda, T., Suzuki, T., & Kato, K. (2008). Prediction of Human Behavior in Human-Robot Interaction Using Psychological Scales for Anxiety and Negative Attitudes Toward Robots. IEEE Transactions on Robotics, 24, 442-451. https://doi.org/10.1109/TRO.2007.914004.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York, NY, USA: McGraw-Hill.
Pellenz, J., Gossow, D., & Paulus, D. (2009). Robbie: A fully autonomous robot for Robocuprescue. Advanced Robotics, 23(9), 1159–1177.
Petter, S., Straub, D., & Rai, A. (2007). Specifying formative constructs in information systems research. MIS Quarterly, 31(4), 623–656.
Politis, I., Langdon, P., Adebayo, D., Bradley, M., Clarkson, P. J., Skrypchuk, L., Mouzakitis, A., Eriksson, A., Brown, J. W., & Revell, K. (2018). An evaluation of inclusive dialogue-based interfaces for the takeover of control in autonomous cars. In Proceedings of 23rd International Conference on Intelligent User Interfaces (pp. 601–606).
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). Smartpls 3. Bönningstedt: Smartpls. Retrieved from http://www.smartpls.com.
Riek, L. D. (2017). Healthcare Robotics. Communications of the ACM, 60(11), 68–78. https://doi.org/10.1145/3127874.
Rivard, S. (2014). Editor’s Comments: The Ions of Theory Construction. MIS Quarterly, 38(2), iii–xiv. https://www.jstor.org/stable/26634928
Ronkainen, S., Häkkilä, J., Kaleva, S., Colley, A., & Linjama, J. (2007). Tap input as an embedded interaction method for mobile devices. In Proceedings of TEI 2007 (pp. 263-270). https://doi.org/10.1145/1226969.1227023
Rosenthal, D. M. (1996). A theory of consciousness. In O. F. N. Block & G. Güzeldere (Eds.), The nature of consciousness. Cambridge, MA, USA: MIT Press.
Schomaker, L., & Hartung, K. (1995) (Eds.). A Taxonomy of Multimodal Interaction in the Human Information Processing System. Report Esprit Project 8579. Accessible at https://www.ai.rug.nl/~lambert/projects/miami/taxonomy/taxonomy.html
Schuetz, S., & Venkatesh, V. (2020). The rise of human machines: How cognitive computing systems challenge assumptions of user-system interaction. Journal of the Association for Information Systems, 21, 460-482. https://doi.org/10.17705/1jais.00608.
Sci2Team. (2009). Science of Science (Sci2) Tool (Indiana University and Scitech Strategies, 2009). Retrieved from http://sci2.cns.iu.edu.
Seddon, P. B. (1997). A respecification and extension of the Delone and Mclean model of IS success. Information Systems Research, 8(3), 240-253.
Sen, S., Chakrabarty, S., Toshniwal, R., & Bhaumik, A. (2015). Design of an intelligent voice controlled home automation system. International Journal of Computer Applications, 121, 39-42.
Smith, A. N., Fischer, E., & Yongjian, C. (2020). How does artificial intelligence become social? A process model of AI integration into consumer practices. Journal of the Academy of Marketing Science, 48(1), 231–250.
Stein, J.-P., Liebold, B., & Ohler, P. (2019). Stay back, clever thing! Linking situational control and human uniqueness concerns to the aversion against autonomous technology. Computers in Human Behavior, 95, 73-82.
Sun, J., & Teng, J. T. C. (2012). Information systems use: Construct conceptualization and scale development. Computers in Human Behavior, 28(5), 1564-1574.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144-176.
Thatcher, J. B., Wright, R. T., Sun, H., Zagenczyk, T. J., & Klein, R. (2018). Mindfulness in information technology use: Definitions, distinctions, and a new measure. MIS Quarterly, 42(3), 831–848.
Trevino, L. K., & Webster, J. (1992). Flow in computer-mediated communication: Electronic mail and voice mail evaluation and impacts. Communication Research, 19(5), 539-557.
Vagia, M., Transeth, A. A., & Fjerdingen, S. A. (2016). A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed? Applied Ergonomics, 53, 190-202.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17, 328–376. https://doi.org/10.17705/1jais.00428.
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Wang, W., & Benbasat, I. (2007). Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs. Journal of Management Information Systems, 23(4), 217-246. https://doi.org/10.2753/MIS0742-1222230410.
Wang, W., & Butler, J. E. (2006). System deep usage in post-acceptance stage: A literature review and a new research framework. International Journal of Business Information Systems, 1(4), 439–462.
Wang, W., Xu, J., & Wang, M. (2018). Effects of recommendation neutrality and sponsorship disclosure on trust vs. distrust in online recommendation agents: Moderating role of explanations for organic recommendations. Decision Support Systems, 114, 1-15.
Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625-636.
Wisdom, J. P., Chor, K. H. B., Hoagwood, K. E., & Horwitz, S. M. (2014). Innovation adoption: A review of theories and constructs. Administration and Policy in Mental Health and Mental Health Services Research, 41(4), 480–502.
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85–102.
Wu, C., Huang, S., & Yuan, Q. (2022). Seven important theories in information system empirical research: A systematic review and future directions. Data and Information Management, 6(1), 100006. https://doi.org/10.1016/j.dim.2022.100006
You, S., & Robert, L. (2018). Emotional Attachment, Performance, and Viability in Teams Collaborating with Embodied Physical Action (EPA) Robots. Journal of the Association for Information Systems, 19, 377-407. https://doi.org/10.17705/1jais.00496.
Zacharaki, A., Kostavelis, I., Gasteratos, A., & Dokas, I. (2020). Safety bounds in human-robot interaction: A survey. Safety Science, 127, 104667.
https://doi.org/10.1016/j.ssci.2020.104667.
Zeitzew, M. A. (2007). Autonomous Utility Mower. Agricultural Engineering International - The CIGR Ejournal, IX(July).
Złotowski, J., Yogeeswaran, K., & Bartneck, C. (2017). Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. International Journal of Human-Computer Studies, 100, 48-54. https://doi.org/10.1016/j.ijhcs.2016.12.008.
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