@article {Dogru2020,
	title = {AI in operations management: applications, challenges and opportunities},
	journal = {Journal of Data, Information and Management},
	volume = {2},
	number = {2},
	year = {2020},
	pages = {67{\textendash}74},
	publisher = {Journal of Data, Information and Management},
	abstract = {We have witnessed unparalleled progress in artificial intelligence (AI) and machine learning (ML) applications in the last two decades. The AI technologies have accelerated advancements in robotics and automation, which have significant implications on almost every aspect of businesses, and especially supply chain operations. Supply chains have widely adopted smart technologies that enable real-time automated data collection, analysis, and prediction. In this study, we review recent applications of AI in operations management (OM) and supply chain management (SCM). Specifically, we consider the innovations in healthcare, manufacturing, and retail operations, since collectively, these three areas represent a majority of the AI innovations in business as well as growing problem areas. We discuss primary challenges and opportunities for utilizing AI in those industries. We also discuss trending research topics with significant value potential in these areas.},
	keywords = {AI, artificial intelligence, Artificial Intelligence (AI), automation, machine learning, machine learning (ML), ml, om, operations management, Operations Management (OM), Robotics, scm, supply chain management, Supply Chain Management (SCM)},
	issn = {2524-6356},
	doi = {10.1007/s42488-020-00023-1},
	author = {Dogru, Ali K and Keskin, Burcu B}
}
@article {Gentili2020,
	title = {Are machines stealing our jobs?},
	journal = {Cambridge Journal of Regions, Economy and Society},
	volume = {13},
	number = {1},
	year = {2020},
	pages = {153{\textendash}173},
	abstract = {This study aims to contribute empirical evidence to the debate about the future of work in an increasingly robotised world. We implement a data-driven approach to study the technological transition in six leading Organisation for Economic Co-operation and Development (OECD) countries. First, we perform a cross-country and cross-sector cluster analysis based on the OECD-STAN database. Second, using the International Federation of Robotics database, we bridge these results with those regarding the sectoral density of robots. We show that the process of robotisation is industry- and country-sensitive. In the future, participants in the political and academic debate may be split into optimists and pessimists regarding the future of human labour; however, the two stances may not be contradictory.},
	keywords = {cluster analysis, e24, e66, j24, jel classifications, labour dislocation, robotisation},
	issn = {1752-1378},
	doi = {10.1093/cjres/rsz025},
	author = {Gentili, Andrea and Compagnucci, Fabiano and Gallegati, Mauro and Valentini, Enzo}
}
@article {Johnson2020,
	title = {The benefits of eHRM and AI for talent acquisition},
	journal = {Journal of Tourism Futures},
	year = {2020},
	abstract = {Purpose : The hospitality and tourism industry faces a number of workforce challenges, especially the high turnover rates and associated replacement costs associated with continually identifying and hiring new employees. The purpose of this paper is to discuss how hospitality and tourism organizations can use electronic human resource management (eHRM) and artificial intelligence (AI) to help recruit and select qualified employees, increase individual retention rates and decrease the time needed to replace employees. Specifically, it discusses how e-recruiting and e-selection and AI tools can help hospitality and tourism organizations improve recruiting and selection outcomes. Design/methodology/approach: Research on eHRM, AI, employee recruitment and employee selection are applied to the hospitality and tourism industry and insights for how eHRM and AI can be applied to the industry are discussed. Findings: eHRM and AI have the potential to transform how the hospitality and tourism industry recruit and select employees. However, care must be taken to ensure that the insights gained and the decisions made are well received by employees and lead to better employee and organizational outcomes. Research limitations/implications: This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry. Originality/value: This paper represents the first research that integrates research from eHRM and AI and applies it to the hospitality and tourism industry.},
	keywords = {artificial intelligence, e-HRM, e-recruiting, e-selection, eHRM, Electronic human resource management, Employee selection, Recruitment, Selection},
	issn = {2055592X},
	doi = {10.1108/JTF-02-2020-0013},
	author = {Johnson, Richard D and Stone, Dianna L and Lukaszewski, Kimberly M}
}
@article {Acemoglu2020,
	title = {Competing with Robots: Firm-Level Evidence from France},
	journal = {AEA Papers and Proceedings},
	volume = {110},
	year = {2020},
	pages = {383{\textendash}388},
	abstract = {We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.},
	keywords = {automation, competition, j23, j24, jel codes, l11, labor share, manufacturing, productivity, reallocation, robots, tasks},
	issn = {2574-0768},
	doi = {10.1257/pandp.20201003},
	author = {Acemoglu, Daron and Lelarge, Claire and Restrepo, Pascual}
}
@article {Huang2020,
	title = {Engaged to a Robot? The Role of AI in Service},
	journal = {Journal of Service Research},
	year = {2020},
	abstract = {This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.},
	keywords = {artificial intelligence, augmentation, automation, engagement, feeling AI, human intelligence, mechanical AI, personalization, relationalization, replacement, robots, service process, service strategy, standardization, thinking AI},
	issn = {15527379},
	doi = {10.1177/1094670520902266},
	author = {Huang, Ming Hui and Rust, Roland T}
}
@article {EganadelSol2020,
	title = {The Future of Work in Developing Economies},
	journal = {MIT Sloan Management Review},
	volume = {61},
	number = {2},
	year = {2020},
	pages = {1{\textendash}3},
	abstract = {<p>Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment. However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies. Here, del Sol and Joyce examine the effects of large-scale automation on workers in developing countries.</p>},
	keywords = {Armenia, Asia, Austria, automation, Bolivia, Business And Economics{\textendash}Management, China, Developing countries{\textendash}LDCs, Employment, future, Georgia (country), Ghana, Impact analysis, Kenya, Kuala Lumpur Malaysia, Laos, Republic of North Macedonia, South Korea, Sri Lanka, United States{\textendash}US, Vietnam, Workers},
	issn = {15329194},
	author = {Egana del Sol, Pablo and Joyce, Connor and Del Sol, Pablo Ega{\~n}a and Joyce, Connor}
}
@article {Seeber2020,
	title = {Machines as teammates: A research agenda on AI in team collaboration},
	journal = {Information and Management},
	volume = {57},
	number = {2},
	year = {2020},
	pages = {103174},
	publisher = {Elsevier},
	abstract = {What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas {\textendash} Machine artifact, Collaboration, and Institution {\textendash} and 17 dualities {\textendash} significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.},
	keywords = {artificial intelligence, Design, Duality, Research agenda, Team collaboration},
	issn = {03787206},
	doi = {10.1016/j.im.2019.103174},
	url = {https://doi.org/10.1016/j.im.2019.103174},
	author = {Seeber, Isabella and Bittner, Eva and Briggs, Robert O and de Vreede, Triparna and de Vreede, Gert Jan and Elkins, Aaron and Maier, Ronald and Merz, Alexander B and Oeste-Rei{\ss}, Sarah and Randrup, Nils and Schwabe, Gerhard and S{\"o}llner, Matthias}
}
@article {Spencer2020,
	title = {No automation please, we{\textquoteright}re British: Technology and the prospects for work},
	journal = {Cambridge Journal of Regions, Economy and Society},
	volume = {13},
	number = {1},
	year = {2020},
	pages = {117{\textendash}134},
	abstract = {This article assesses the impact and probably limits of automation. It looks, in particular, at the case of the UK economy. The prospects for automation are seen as necessarily uncertain and potentially regressive in their effects, with technology likely to sustain a large number of low-quality jobs. The deep-seated problems of the UK economy-low-investment, low-productivity and low-real wages-are seen as key impediments to forms of automation that work for all in society. It is argued that, without wider institutional reform, the UK will be unable to reap the full potential of automation.},
	keywords = {automation, investment, robots, technology, work},
	issn = {17521386},
	doi = {10.1093/cjres/rsaa003},
	author = {Spencer, David and Slater, Gary}
}
@booklet {Willcocks2020,
	title = {Robo-Apocalypse cancelled? Reframing the automation and future of work debate},
	howpublished = {Journal of Information Technology},
	number = {2016},
	year = {2020},
	abstract = {Robotics and the automation of knowledge work, often referred to as AI (artificial intelligence), are presented in the media as likely to have massive impacts, for better or worse, on jobs skills, organizations and society. The article deconstructs the dominant hype-and-fear narrative. Claims on net job loss emerge as exaggerated, but there will be considerable skills disruption and change in the major global economies over the next 12 years. The term AI has been hijacked, in order to suggest much more going on technologically than can be the case. The article reviews critically the research evidence so far, including the author{\textquoteright}s own, pointing to eight major qualifiers to the dominant discourse of major net job loss from a seamless, overwhelming AI wave sweeping fast through the major economies. The article questions many assumptions: that automation creates few jobs short or long term; that whole jobs can be automated; that the technology is perfectible; that organizations can seamlessly and quickly deploy AI; that humans are machines that can be replicated; and that it is politically, socially and economically feasible to apply these technologies. A major omission in all studies is factoring in dramatic increases in the amount of work to be done. Adding in ageing populations, productivity gaps and skills shortages predicted across many G20 countries, the danger might be too little, rather than too much labour. The article concludes that, if there is going to be a Robo-Apocalypse, this will be from a collective failure to adjust to skills change over the next 12 years. But the debate needs to be widened to the impact of eight other technologies that AI insufficiently represents in the popular imagination and that, in combination, could cause a techno-apocalypse.},
	keywords = {AI, automation, cognitive automation, future of work, Information Technology, Jobs, robotic process automation, skills},
	isbn = {0268396220925},
	issn = {14664437},
	doi = {10.1177/0268396220925830},
	author = {Willcocks, Leslie}
}
@article {Arduengo2020,
	title = {The Robot Economy : Here It Comes},
	journal = {International Journal of Social Robotics},
	number = {July},
	year = {2020},
	publisher = {Springer Netherlands},
	keywords = {blockchain, Cloud robotics, Intelligent robots, IoRT, Robot economy},
	issn = {1875-4805},
	doi = {10.1007/s12369-020-00686-1},
	url = {https://doi.org/10.1007/s12369-020-00686-1},
	author = {Arduengo, Miguel and Sentis, Luis}
}
@article {Petersen2020,
	title = {The Role of Discretion in the Age of Automation},
	journal = {Computer Supported Cooperative Work: CSCW: An International Journal},
	volume = {29},
	number = {3},
	year = {2020},
	pages = {303{\textendash}333},
	abstract = {This paper examines the nature of discretion in social work in order to debunk myths dominating prevalent debates on digitisation and automation in the public sector. Social workers have traditionally used their discretion widely and with great autonomy, but discretion has increasingly come under pressure for its apparent subjectivity and randomness. In Denmark, our case in point, the government recently planned to standardise laws to limit or remove discretion where possible in order for automation of case management to gain a foothold. Recent studies have focused on discretion in the public sector, but few have examined it explicitly and as part of real cases. As a consequence, they often leave the myths about discretion unchallenged. Inspired by the literature on discretion and CSCW research on rules in action, this study reports on an empirical investigation of discretion in child protection services in Denmark. The results of our analysis provide a new understanding of discretion as a cooperative endeavour, based on consultation and skill, rather than an arbitrary or idiosyncratic choice. In this manner, our study contradicts the myth of discretion inherent in the automation agenda. Correspondingly, we ask for attention to be given to systems that integrate discretion with technology rather than seek to undermine it directly or get around it surreptitiously. In this age of automation, this is not only an important but also an urgent task for CSCW researchers to fulfil.},
	keywords = {Administrative work, automation, Casework, Decision-Making, Digital-ready legislation, Digitisation, Discretion, Rules in action, Social work},
	issn = {15737551},
	doi = {10.1007/s10606-020-09371-3},
	author = {Petersen, Anette C.M. and Christensen, Lars Rune and Hildebrandt, Thomas T.}
}
@article {Theresa2020,
	title = {Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation},
	journal = {The TQM Journal},
	volume = {ahead-of-p},
	number = {ahead-of-print},
	year = {2020},
	month = {jan},
	abstract = {Purpose This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.Design/methodology/approach Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.Findings Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business{\textquoteright} organizational model. A key consideration is a {\textquotedblleft}creative-possibility perspective,{\textquotedblright} highlighting the future potential to use AI not only for rational but also for creative thinking purposes.Research limitations/implications This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a {\textquotedblleft}creative-possibility perspective.{\textquotedblright}Practical implications A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).Originality/value This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.},
	keywords = {AI, artificial intelligence, creativity, marketing strategy, marketing synergy, paper type research paper, rationality, tqm},
	isbn = {1754-2731},
	doi = {10.1108/TQM-12-2019-0303},
	url = {https://doi.org/10.1108/TQM-12-2019-0303},
	author = {Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera and Eriksson, Theresa and Bigi, Alessandro and Bonera, Michelle and Theresa, Eriksson and Alessandro, Bigi and Michelle, Bonera}
}
@article {2019,
	title = {Will artificial intelligence take over human resources recruitment and selection?},
	journal = {Network Intelligence Studies},
	volume = {VII},
	year = {2019},
	keywords = {artificial intelligence, human resources information systems, recruitment and selection},
	url = {https://ideas.repec.org/a/cmj/networ/y2019i13p21-30.html},
	author = {Bilal Hmoud and Varallya Laszlo}
}
@article {2018,
	title = {Artificial Intelligence: A Case Study on Risk Mitigation},
	journal = {International Journal of Computer Science and Network Security},
	volume = {18},
	year = {2018},
	month = {04/2018},
	pages = {9-12},
	abstract = {Artificial intelligence technologies are expanding at an extraordinary rate with wide range of applications from machine translation to medical image analysis. Many applications are being developed and there is no doubt that in the near future this will create substantial risk for humanity. In addition, Artificial intelligence is a dual use are of technology as it can be used toward useful of harmful ends, for example autonomous drones can be used to deliver packages faster and easier and on the other hand, they can be used to deliver explosives. This literature review research investigates different types of artificial intelligence risks and proposes ways to mitigate these risks.},
	keywords = {AI application, artificial intelligence, Risk mitigation},
	author = {Ahmed AL-Gindy}
}
@article {2018,
	title = {Artificial Intelligence in Medicine and Radiation Oncology},
	journal = {Cureus},
	year = {2018},
	month = {Jan-04-2019},
	abstract = {Artificial Intelligence (AI) was reviewed with a focus on its potential applicability to radiation oncology. The improvement of process efficiencies and the prevention of errors were found to be the most significant contributions of AI to radiation oncology. It was found that the prevention of errors is most effective when data transfer processes were automated and operational decisions were based on logical or learned evaluations by the system. It was concluded that AI could greatly improve the efficiency and accuracy of radiation oncology operations.},
	keywords = {artificial intelligence, big data, error analysis, error prevention, machine learning, process efficiency, process optimization, quality improvement, radiation oncology},
	doi = {10.7759/cureus.2475},
	url = {https://www.cureus.com/articles/11443-artificial-intelligence-in-medicine-and-radiation-oncology},
	author = {Weidlich, Vincent and Weidlich, Georg A.}
}
@article {2018,
	title = {Automated Classification of chest X-ray images as normal or abnormal using Convolutional Neural Network},
	journal = {Asian Journal of Convergence in Technology},
	volume = {4},
	year = {2018},
	month = {04/2018},
	abstract = {Chest X-Rays are generally used for diagnosing abnormalities in the thoracic area. Radiologists need to spend significant amount of time for interpreting scans. Automatic classification of these images could greatly help radiology interpretation process by enhancing real world diagnosis of problems. Hence, radiologists can focus on detecting abnormalities from the abnormal images rather than checking for it in all the images. In this paper, we present a machine learning approach to solve this problem. Here, the algorithm uses COnvolutional Neural Networks (CNN) to learn and classify chest X-ray images as normal or abnormal based on image features.},
	keywords = {classification, machine learning, radiology},
	author = {Aayushi Gupta and Anupama C and P Indumathi and Anuj Kumar}
}
@article {2018,
	title = {Cognitive and technological aspects of e-learning in context of robotization},
	journal = {Cognitive Science {\textendash} New Media {\textendash} Education},
	volume = {3},
	year = {2018},
	month = {Mar-06-2020},
	pages = {65},
	abstract = {The development of e-learning education led to the emergence of two main problems of such form of education, which are the user-system interaction from the cognitive prospective, and the analysis of massive data received out of students{\textquoteright} activity.  The development of artificial intelligence concepts and robotic process automation (RPA) tools, both problems might be solved in a more efficient way. The article presents the intelligent system of feedbacks, realized as JavaScript extension to Moodle platform, which intends to strength cognitive output of the preformed learning activity, creating an illusion of the trainer{\textquoteright}s presence and, therefore, contribute to the resolution of the first problem. The resolution of the second problem is proposed using artificial intelligence worker built in Blue Prism RPA platform, which performs validation of test questions upon strict criteria of selection. Such validation process allows to select the questions which are coherent with the index of complexity and the index of differentiation capacity.},
	keywords = {artificial intelligence worker, Blue Prism automation, intelligent system of feedbacks, robotic process automation},
	issn = {2543-506X},
	doi = {10.12775/CSNME.2017.013},
	url = {http://apcz.umk.pl/czasopisma/index.php/CSNME/article/view/CSNME.2017.013},
	author = {Shvets, Anna and Shvets, Valentyna}
}
@article {2018,
	title = {The discourse approach to boundary identification and corpus construction for theory review articles},
	journal = {Journal of the Association for Information Systems},
	year = {2018},
	keywords = {article identification, boundary identification, citation search, keyword search, literature review, machine learning, research review, review article},
	url = {https://www.researchgate.net/publication/325215971_Understanding_the_Elephant_The_Discourse_Approach_to_Boundary_Identification_and_Corpus_Construction_for_Theory_Review_Articles},
	author = {Kai R. Larsen and Dirik S. Hovorka and Alan R. Dennis and Jevin D. West}
}
@article {2018,
	title = {How real is the impact of artificial intelligence? The business information survey 2018},
	journal = {Business Information Review},
	volume = {35},
	year = {2018},
	month = {12/2019},
	pages = {99 - 115},
	keywords = {Artificial Intelligence (AI), blockchain, chatbot, cybersecurity, data economy, data governance, data lakes, data literacy, data quality, data trusts, data value, ethics, information literacy, intelligent virtual agents, machine learning (ML), Robotics},
	issn = {0266-3821},
	doi = {10.1177/0266382118790150},
	url = {http://journals.sagepub.com/doi/10.1177/0266382118790150},
	author = {Carter, Denise}
}
@article {2018,
	title = {Robots and Organization Studies: Why Robots Might Not Want to Steal Your Job},
	journal = {Organization Studies},
	year = {2018},
	month = {04/2018},
	pages = {017084061876556},
	edition = {2},
	abstract = {A number of recent high-profile studies of robotics and artificial intelligence (or AI) in economics and sociology have predicted that many jobs will soon disappear due to automation, with few new ones replacing them. While techno-optimists and techno-pessimists contest whether a jobless future is a positive development or not, this paper points to the elephant in the room. Despite successive waves of computerization (including advanced machine learning), jobs have not disappeared. And probably won{\textquoteright}t in the near future. To explain why, some basic insights from organization studies can make a contribution. I propose the concept of {\textquoteright}bounded automation{\textquoteright} to demonstrate how organizational forces mould the application of technology in the employment sector. If work does not vanish in the age of AI, then poorly paid jobs will most certainly proliferate, I argue. Finally, a case is made for the scholarly community to engage with wider social justice concerns. This I term public organization studies.},
	keywords = {artificial intelligence, bounded automation, neoliberalism, public organization studies, Robotics, unemployment, work},
	issn = {0170-8406},
	doi = {10.1177/0170840618765568},
	url = {http://journals.sagepub.com/doi/10.1177/0170840618765568},
	author = {Fleming, Peter}
}
@article {2018,
	title = { Robots worldwide: The impact of automation on employment and trade},
	number = {36},
	year = {2018},
	month = {10/2018},
	keywords = {economics of automation, Employment, off-shoring, re-shoring, robot, technology},
	url = {https://www.ilo.org/wcmsp5/groups/public/---dgreports/---inst/documents/publication/wcms_648063.pdf},
	author = {Francesco Carnonero and Ekkehard Ernst and Enzo Weber}
}
@article {2017,
	title = {Seeing Like a Tesla: How Can We Anticipate Self-Driving Worlds?},
	journal = {Glocalism},
	volume = {3},
	year = {2017},
	abstract = {In the last five years, investment and innovation in self-driving cars has accelerated dramatically. Automotive autonomy, once seen as impossible, is now sold as inevitable. Much of the governance discussion has centred on risk: will the cars be safer than their human-controlled counterparts? As with conventional cars, harder long-term questions relate to the future worlds that self-driving technologies might enable or even demand. The vision of an autonomous vehicle {\textendash} able to navigate the world{\textquoteright}s complexity using only its sensors and processors {\textendash} on offer from companies like Tesla is intentionally misleading. So-called {\textquotedblleft}autonomous{\textquotedblright} vehicles will depend upon webs of social and technical connectivity. For their purported benefits to be realised, infrastructures that were designed around humans will need to be upgraded in order to become machine-readable. It is vital to anticipate the politics of self-driving worlds in order to avoid exacerbating the inequalities that have emerged around conventional cars. Rather than being dazzled by the Tesla view, policymakers should start seeing like a city, from multiple perspectives. Good governance for self-driving cars means democratising experimentation and creating genuine collaboration between companies and local governments.

 },
	keywords = {automotive autonomy, governance, risk, self-driving cars, Tesla},
	doi = {10.12893/gjcpi.2017.3.2},
	author = {Jack Stilgoe}
}
