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Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements
Abstract
Increasing application areas and depths of autonomous systems in logistics provide a new level of challenge for the analysis and design of human–machine interaction concepts. Due to scarce high-skilled personnel in several regions and the objectives of efficiency and sustainability improvement, logistics operators have to pursue technological progress like automation with all means. In order to distinguish between more or less performing human–artificial collaboration systems in logistics ex ante for investment decision purposes, a multi-dimensional conceptual framework is developed. A comprehensive case study regarding automated truck driving in logistics is provided in order to test the concept concerning practical implications. Results include the notion of four distinctive and increasing resistance levels before finally an efficient ‘trusted’ collaboration between human operators and artificial intelligence systems can be achieved. This is important for the design of many automated systems in logistics, among others for driving and piloting professions regarding autonomous driving supervision.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Skill Content of Recent Technological Change: An Empirical Exploration
Autor, D. H., Levy, F., & Murnane, R. J. (2003) · The Quarterly Journal of Economics · doi:10.1162/003355303322552801 · Cited by 6,205
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 1×
“…on the artificial side of the collaboration which can be used to lower resistance (e.g. design and speech variation of automated applications as female navigation system voice tones, comparable insights, see Bazilinskyy and de Winter 2015; Baldwin 2011; Parasuraman, Sheridan, and Wickens 2000)?”
MethodPreparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations
Fagnant, D. J., & Kockelman, K. (2015) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2015.04.003 · Cited by 3,214
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
Bainbridge, L. (1983) · Automatica · doi:10.1016/0005-1098(83)90046-8 · Cited by 2,205
Stevens, G. C. (1989) · International Journal of Physical Distribution & Materials Management · doi:10.1108/eum0000000000329 · Cited by 1,560
Sustainable procurement in the United Kingdom public sector
Walker, H., & Brammer, S. (2009) · Supply Chain Management: An International Journal · doi:10.1108/13598540910941993 · Cited by 603
Pinar Saygin, A., Cicekli, I., & Akman, V. (2000) · Minds and Machines · doi:10.1023/a:1011288000451 · Cited by 383
Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles
Childress, S., Nichols, B., Charlton, B., & Coe, S. (2015) · Transportation Research Record: Journal of the Transportation Research Board · doi:10.3141/2493-11 · Cited by 319
Research works printed but not linked: 125
- 10.1016/j.omega.2014.05.009 · DOI · Paper page
- 10.1109/MCS.2015.2471046 · DOI · Paper page
- 10.1007/s12544-017-0248-6 · DOI
- Armstrong S., 2014, Smarter Than Us – The Rise of Machine Intelligence
- 10.1016/j.tre.2015.01.014 · doi:10.1016/j.tre.2015.01.014
- 10.1080/00140139.2011.558634 · DOI
- Barrat J., 2013, Our Final Invention – Artificial Intelligence and the End of the Human Era
- 10.1016/j.apergo.2017.05.001 · DOI
- 10.1177/001979390105400227 · DOI
- Bergenheim C., 2012, Proceedings of the 19th ITS World Congress
- 10.1108/13552550210448348 · DOI
- 10.1080/13675567.2015.1015508 · DOI
- 10.1108/JHLSCM-08-2012-0019 · doi:10.1108/jhlscm-08-2012-0019
- 10.1108/09574090110806316 · doi:10.1108/09574090110806316
- Bostrom N., 2014, Superintelligence – Paths, Dangers, Strategies
- 10.1007/978-3-319-56541-5_58 · doi:10.1007/978-3-319-56541-5_58
- 10.1016/j.conengprac.2016.04.013 · DOI · Paper page
- 10.1080/00207540701266807 · DOI
- 10.1108/09600030810882816 · DOI · Paper page
- Center for Global Policy Solutions, 2017, Stick Shift: Autonomous Vehicles, Driving Jobs, and the Future of Work
- 10.1108/13598540910995192 · doi:10.1108/13598540910995192
- 10.1016/j.aap.2015.09.001 · doi:10.1016/j.aap.2015.09.001
- 10.1007/978-1-4471-2280-7 · DOI
- 10.1108/09574090610689998 · DOI · Paper page
- 10.1108/09574090710748153 · DOI
- 10.1016/j.tre.2005.09.007 · DOI · Paper page
- 10.1016/j.tbs.2017.04.001 · DOI
- Dawson D., 2014, Medicine Reviews, 18, 141
- 10.1016/j.compchemeng.2015.02.003 · doi:10.1016/j.compchemeng.2015.02.003
- 10.1016/j.ejor.2003.10.030 · DOI
- Fagnant D. J., 2015, Transportation Research Part A, 77, 167
- Fawcett S. E., 1995, Journal of Business Logistics, 16, 299
- 10.1016/j.trc.2015.03.035 · DOI
- 10.1108/01443570710714556 · DOI
- Fox N., 2002, Against the Machine – The Hidden Luddite Tradition in Literature, Art, and Individual Lives
- 10.1007/s10514-017-9619-z · DOI · Paper page
- 10.1002/j.2158-1592.2001.tb00002.x · doi:10.1002/j.2158-1592.2001.tb00002.x
- 10.1016/j.enconman.2015.10.006 · DOI
- 10.1016/j.trc.2016.01.002 · DOI
- 10.1504/IJLSM.2006.009554 · doi:10.1504/ijlsm.2006.009554
- Graf R., 2014, IEEE intelligent Vehicles Symposium Proceedings, 939
- 10.1016/j.ijpe.2013.12.011 · DOI · Paper page
- Harnad S., 1992, ACM SIGART Bulletin (Association for Computing Machinery, Special Interest Group on Artificial Intelligence), 3, 9
- 10.1016/j.techfore.2015.12.005 · DOI
- 10.1038/544413a · DOI
- 10.1016/j.procs.2017.03.074 · doi:10.1016/j.procs.2017.03.074
- 10.1111/j.1937-5956.2005.tb00008.x · DOI · Paper page
- 10.5267/j.ijiec.2012.08.004 · DOI
- Huang A. S., 2016, Robotics Research. Springer Tracts in Advanced Robotics, 235
- 10.1016/j.cie.2005.01.001 · DOI
- Jones S. E., 2006, Against Technology: From the Luddites to Neo-Luddism
- Kalra N., 2016, Transportation Research Part A, 94, 182
- 10.1109/MITS.2013.2278405 · DOI
- Klumpp M., 2014, Logistics and Demographic Change
- 10.1108/SL-12-2016-0085 · DOI
- Königs K. D., 2015, Transformative Perspectives and Processes in Higher Education, 117, 10.1007/978-3-319-09247-8_7 · DOI
- 10.1007/s12008-014-0227-2 · DOI · Paper page
- 10.1504/IJVAS.2012.051270 · DOI · Paper page
- Kuhnt F., 2016, IEEE Intelligent Vehicles Symposium Proceedings, 1271
- 10.1177/01454455960204002 · DOI
- 10.1038/nature21492 · DOI
- 10.1016/j.jbusres.2006.03.003 · DOI · Paper page
- 10.1016/j.mechatronics.2014.01.007 · DOI
- 10.1016/j.trc.2015.10.005 · DOI · Paper page
- 10.1504/IJSOM.2012.050143 · DOI
- 10.1007/978-1-4020-9911-3 · doi:10.1007/978-1-4020-9911-3
- 10.1016/j.econmod.2013.06.041 · DOI
- 10.1016/j.ijpe.2014.12.026 · doi:10.1016/j.ijpe.2014.12.026
- 10.1002/j.2158-1592.2008.tb00072.x · DOI · Paper page
- 10.1108/09600031111138844 · DOI · Paper page
- 10.1016/S0968-090X(00)00022-X · DOI · Paper page
- 10.1016/j.jadohealth.2015.02.013 · DOI
- 10.1108/09600031311293228 · DOI
- 10.1016/j.procs.2011.08.023 · doi:10.1016/j.procs.2011.08.023
- 10.1108/09600030310469153 · doi:10.1108/09600030310469153
- 10.1016/S0020-7373(87)80013-5 · DOI · Paper page
- Naranjo J., 2007, Journal of Intelligent Systems, 22, 36
- Nasirian F., 2017, AI-Based Voice Assistant Systems: Evaluating From the Interaction and Trust Perspectives
- 10.1038/nrcardio.2015.34 · DOI · Paper page
- 10.1016/j.ymssp.2016.12.017 · DOI
- 10.1007/978-3-319-56185-1_11 · doi:10.1007/978-3-319-56185-1_11
- 10.1016/j.ejor.2013.04.042 · DOI
- 10.1016/j.promfg.2015.07.824 · DOI
- 10.1016/j.rtbm.2014.03.001 · doi:10.1016/j.rtbm.2014.03.001
- 10.1016/j.promfg.2015.07.764 · DOI
- 10.1016/j.aap.2014.11.021 · DOI · Paper page
- 10.1016/j.sbspro.2014.01.1471 · DOI
- 10.1016/j.image.2017.02.007 · DOI
- 10.1016/j.tre.2015.01.004 · DOI
- 10.1016/j.jmsy.2015.03.001 · DOI · Paper page
- 10.1016/j.jom.2007.01.020 · DOI · Paper page
- 10.1016/j.promfg.2015.07.152 · DOI
- Rezvani T., 2016, 2016 IEEE International Conference on Intelligent Transportation Systems Proceedings, 682
- 10.1016/j.asoc.2016.02.026 · DOI
- 10.1108/IJPPM-11-2013-0185 · DOI
- Schakel W. J., 2010, 2010 IEEE Conference on Intelligent Transportation Systems (ITSC) Proceedings, 759
- 10.1016/j.neunet.2014.09.003 · DOI · Paper page
- 10.1016/j.jngse.2015.03.009 · DOI
- Shankwitz C., 2017, Long-haul Truck Freight Transport and the Role of Automation: Collaborative Human – Automated Platooned Trucks Alliance (CHAPTA)
- 10.1016/j.indmarman.2008.11.005 · DOI · Paper page
- 10.1016/j.trb.2014.06.016 · DOI
- 10.1080/13675567.2012.709488 · doi:10.1080/13675567.2012.709488
- 10.1108/09600030510577421 · DOI · Paper page
- 10.1108/13598540810860994 · DOI · Paper page
- 10.1080/15389588.2016.1199865 · DOI
- 10.1016/j.trc.2016.07.007 · DOI · Paper page
- 10.1016/j.asoc.2015.12.005 · DOI · Paper page
- 10.1016/j.tre.2015.04.004 · DOI
- Tsuji S., 2015, Cancer Research, 75, B1
- Turing A., 1952, The Essential Turing – Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence and Artificial Life, 487
- 10.1080/13675567.2016.1219703 · DOI
- 10.1111/odi.12668 · DOI
- 10.1057/palgrave.jors.2602125 · DOI · Paper page
- 10.1177/0018720812443825 · DOI · Paper page
- 10.1016/j.ejor.2009.06.005 · doi:10.1016/j.ejor.2009.06.005
- 10.1504/IJLSM.2009.022497 · DOI
- 10.1016/j.ssci.2014.09.004 · DOI
- 10.1016/j.trpro.2016.06.015 · doi:10.1016/j.trpro.2016.06.015
- 10.1016/j.chb.2012.07.027 · DOI
- 10.1016/j.omega.2007.01.007 · doi:10.1016/j.omega.2007.01.007
- 10.1299/kikaic.77.4125 · doi:10.1299/kikaic.77.4125
- 10.3389/fnhum.2017.00078 · DOI
- 10.1016/j.jclepro.2013.10.036 · doi:10.1016/j.jclepro.2013.10.036
- 10.1038/ctg.2016.9 · DOI
- 10.1007/978-3-319-22288-2 · doi:10.1007/978-3-319-22288-2
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Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.09.009 · Cited by 1,020
“Knoll et al. (2016) adopt a predictive inbound logistics planning approach, whereas Klumpp (2018) develops a multi-dimensional conceptual framework to distinguish between better-and worse-performing human – artificial collaboration systems in logistics.”
MethodBelhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024) · Annals of Operations Research · doi:10.1007/s10479-021-03956-x · Cited by 797
Technology readiness and the organizational journey towards AI adoption: An empirical study
Uren, V., & Edwards, J. S. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102588 · Cited by 335
Elahi, M., Afolaranmi, S. O., Martinez Lastra, J. L., & Perez Garcia, J. A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00089-x · Cited by 303
Di Vaio, A., Boccia, F., Landriani, L., & Palladino, R. (2020) · Sustainability · doi:10.3390/su12124851 · Cited by 292
Metadata
- Authors
- Klumpp, M.
- Venue
- International Journal of Logistics Research and Applications
- Publisher
- Informa UK Limited
- Published
- 2017-10-03
- Volume
- 21
- Issue
- 3
- Pages
- 224-242
- ISSN
- 1367-5567 (ISSN-L)
- 1469-848X
- OpenAlex
- W2761948312
- Cited by
- 211
- References
- 11
- FWCI
- 8.64
- Type
- journal article
- Language
- en
- Open access
- closed
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