Understanding the implications of artificial intelligence on field service operations: a case study of BT
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[1] Thomas Y. Choi,et al. Renaissance of case research as a scientific method , 2014 .
[2] Thomas Magnusson,et al. Transition pathways revisited: Established firms as multi-level actors in the heavy vehicle industry , 2015 .
[3] Sun Jin Kim,et al. Balancing and sequencing mixed-model U-lines with a co-evolutionary algorithm , 2000 .
[4] Ibrahim Kucukkoc,et al. A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem , 2015 .
[5] Angappa Gunasekaran,et al. Determinants of RFID adoption intention by SMEs: an empirical investigation , 2016 .
[6] R. Cooper,et al. Benchmarking the Firm's Critical Success Factors in New Product Development , 1995 .
[7] Rustam M. Vahidov,et al. Application of machine learning techniques for supply chain demand forecasting , 2008, Eur. J. Oper. Res..
[8] A. Neely,et al. Measuring performance in a changing business environment , 2003 .
[9] J. Dhaliwal,et al. An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management , 2009 .
[10] Hokey Min,et al. Artificial intelligence in supply chain management: theory and applications , 2010 .
[11] Mary J. Benner,et al. Reflections on the 2013 Decade Award—“Exploitation, Exploration, and Process Management: The Productivity Dilemma Revisited” Ten Years Later , 2015 .
[12] Torbjørn H. Netland,et al. Critical success factors for implementing lean production: the effect of contingencies , 2015 .
[13] S. G. Deshmukh,et al. Critical success factors of TQM: A select study of Indian organizations , 2003 .
[14] G. Antoniou,et al. Supply chain risk management and artificial intelligence: state of the art and future research directions , 2018, Int. J. Prod. Res..
[15] J. Rowley. Using case studies in research , 2002 .
[16] Mike Wright,et al. Of robots, artificial intelligence, and work , 2017 .
[17] Maurizio Bielli,et al. New operations research and artificial intelligence approaches to traffic engineering problems , 1996 .
[18] Ben Light,et al. A Critical Success Factors Model for ERP Implementation , 1999, IEEE Softw..
[19] Fotios Pasiouras,et al. Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A Survey , 2009, Eur. J. Oper. Res..
[20] Michael J. Shaw,et al. An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems , 1989 .
[21] Dawn Song,et al. Robust Physical-World Attacks on Deep Learning Models , 2017, 1707.08945.
[22] Fred D. Davis. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..
[23] Elizabeth J. Altman,et al. Disruptive Innovation: An Intellectual History and Directions for Future Research , 2018, Journal of Management Studies.
[24] Matthias Klumpp,et al. Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements , 2018 .
[25] Yogesh Kumar Dwivedi,et al. Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda , 2019, Int. J. Inf. Manag..
[26] Shouhong Wang,et al. The Implementation of Business Process Reengineering , 1995, J. Manag. Inf. Syst..
[27] A. M. Turing,et al. Can Automatic Calculating Machines Be Said to Think , 2004 .
[28] Fred D. Davis,et al. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.
[29] R. Kapoor,et al. Decoding the Adaptability–Rigidity Puzzle: Evidence from Pharmaceutical Incumbents’ Pursuit of Gene Therapy and Monoclonal Antibodies , 2014 .
[30] J. Penrod,et al. A Discussion of Chain Referral As a Method of Sampling Hard-to-Reach Populations , 2003, Journal of transcultural nursing : official journal of the Transcultural Nursing Society.
[31] D. R. Towill,et al. Successful business systems engineering. I. The systems approach to business processes , 1997 .
[32] R. Shankar,et al. Modelling critical success factors for sustainability initiatives in supply chains in Indian context using Grey-DEMATEL , 2018 .
[33] Francisco J. Martínez-López,et al. Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights , 2013 .
[34] In Lee,et al. Machine learning for enterprises: Applications, algorithm selection, and challenges , 2020 .
[35] Joerg S. Hofstetter,et al. Critical factors for sub-supplier management: A sustainable food supply chains perspective , 2014 .
[36] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[37] Andrew B. Whinston,et al. Artificial Intelligence in Manufacturing Planning and Control , 1980 .
[38] Thomas Magnusson,et al. Technological discontinuities and the challenge for incumbent firms: Destruction, disruption or creative accumulation? , 2013 .
[39] T. Cresswell. Towards a Politics of Mobility , 2010 .
[40] S. Pettit,et al. Critical success factors in the context of humanitarian aid supply chains , 2009 .
[41] M. Tushman,et al. Ambidexterity as a Dynamic Capability: Resolving the Innovator's Dilemma , 2007 .
[42] Shahriar Akter,et al. Understanding supply chain analytics capabilities and agility for data-rich environments , 2019 .
[43] Damien Trentesaux,et al. Hybrid approach to decision-making for job-shop scheduling , 1999 .
[44] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[45] Joseph Sarkis,et al. A grey-based DEMATEL model for evaluating business process management critical success factors , 2013 .
[46] Oisín Tansey,et al. Process Tracing and Elite Interviewing: A Case for Non-probability Sampling , 2007, PS: Political Science & Politics.
[47] Klaus-Dieter Thoben,et al. Machine learning in manufacturing: advantages, challenges, and applications , 2016 .
[48] Gordon B. Davis,et al. User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..
[49] Gian Antonio Susto,et al. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach , 2015, IEEE Transactions on Industrial Informatics.
[50] Zhaohan Sheng,et al. Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system , 2009, Expert Syst. Appl..
[51] H. Altay Güvenir,et al. Multicriteria inventory classification using a genetic algorithm , 1998, Eur. J. Oper. Res..
[52] Piotr Soja,et al. Understanding determinants of enterprise system adoption success: lessons learned from full-scope projects in manufacturing companies , 2010 .