@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 and the future of work: Human-AI symbiosis in organizational decision making},
	journal = {Business Horizons},
	volume = {61},
	year = {2018},
	publisher = {Elsevier},
	edition = {586},
	chapter = {577},
	abstract = {Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI and examines how each can bring their own strength in organizational decision-making process typically characterized by uncertainty, complexity, and equivocality. WIth a greater computational information processing capacity and an analytical approach, AI can extend humans{\textquoteright} cognition when addressing complexity, whereas human can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision making. This premise mirrors the idea of intelligence augmentation, which states that AI systems should be designed with the intention of augmenting, not replacing, human contribution.},
	keywords = {Analytical and intuitive decision making, artificial intelligence, Human augmentation, Human-machine symbiosis, Organization decision making},
	doi = {10.1016/j.bushor.2018.03.007},
	author = {Mohammad Hossein Jarrahi}
}
@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 = {Artificial intelligence, machine learning and the evolution of healthcare A bright future or cause for concern?},
	journal = {British Journal of Radiology},
	volume = {7},
	year = {2018},
	pages = {223-225},
	type = {Editorial},
	keywords = {artificial intelligence, deep learning, machine learning},
	author = {L.D. Jones and D. Golan and S. A. Hanna and M. Ramachandran}
}
@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 = {Computational power and the social impact of artificial intelligence},
	journal = {SSRN Electronic Journal},
	year = {2018},
	abstract = {Machine learning is computational process. To that end, it is inextricably tied to computational power - the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computational power and computing architectures shape the speed of training and inference in machine learning, and therefore influence the rate of progress in the technology. But, these relationships are more nuanced than that: hardware shapes the methods used by researchers and engineers in the design and development of machine learning models. Characteristics such as the power consumption of chips also define where and how machine learning can be used in the real world.
In a broader perspective, computational power is also important because of its specific geographies. Semiconductors are designed, fabricated, and deployed through a complex international supply chain. Market structure and competition among companies in this space influence the progress of machine learning. Moreover, since these supply chains are also considered significant from a national security perspective, hardware becomes an arena in which government industrial and trade policy has a direct impact on the fundamental machinery necessary for artificial intelligence (AI).
This paper aims to dig more deeply into the relationship between computational power and the development of machine learning. Specifically, it examines how changes in computing architectures, machine learning methodologies, and supply chains might influence the future of AI. In doing so, it seeks to trace a set of specific relationship between this underlying hardware layer and the broader social impacts and risks around AI. On the hand, this examination shines a spotlight on how hardware works to exacerbate a range of concerns around ubiquitous surveillance, technological unemployment, and geopolitical conflict. On the other, it also highlights the potentially significant role that shaping the development of computing power might play in addressing these concerns.},
	doi = {10.2139/ssrn.3147971},
	author = {Tim Hwang}
}
@book {2018,
	title = {Data Science for Undergraduates},
	year = {2018},
	publisher = {National Academies Press},
	organization = {National Academies Press},
	address = {Washington, D.C.},
	isbn = {978-0-309-47559-4},
	doi = {10.17226/25104},
	url = {https://www.nap.edu/catalog/25104}
}
@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}
}
@proceedings {2018,
	title = {A literature Analysis of Research on Artificial Intelligence in Management Information System (MIS)},
	year = {2018},
	abstract = {This article presents an overview of Artificial Intelligence (AI) research applied in the Management Information System field by analyzing the databases of the AIS journal basket list representing the major international journal on MIS. AN initial set of 438 published papers was identified based on our search criteria. From those, 74 were selected because of their appropriate fit with the research questions. They were organized into domains, type of research, data collection strategy and AI techniques. We found; (1) MIS Quarterly published 42\% of the papers; (2) 50\% of studies were on Information Systems applications, followed by 15\% on Knowledge Management; and (3) 7 AI techniques were presented. This paper provides an overview of applied AI in Management, highlights the most studies related topics and techniques and contributes to a research agenda on the subject.},
	keywords = {artificial intelligence, Information Systems, Literature Analysis},
	author = {Alexandre Moreira Nascimento and Maria Alexandra V. C. da Cunha and Fernando de Souza Meirelles and Eusebio Scornavacca and Vinicius Veloso de Melo}
}
@article {2018,
	title = {Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR)},
	journal = {Systematic Reviews},
	year = {2018},
	month = {2018},
	publisher = {Springer},
	type = {Review},
	abstract = {Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced new algorithms that can accurately mimic human endeavour in systematic review activity, faster and more cheaply. Automation tools need to be able to work together, to exchange data and results. Therefore, we initiated the International Collaboration for the Automation of Systematic Reviews (ICASR), to successfully put all the parts of automation of systematic review production together. The first meeting was held in Vienna in October 2015. We established a set of principles to enable tools to be developed and integrated into toolkits.
This paper sets out the principles devised at that meeting, which cover the need for improvement in efficiency of SR tasks, automation across the spectrum of SR tasks, continuous improvement, adherence to high quality standards, flexibility of use and combining components, the need for a collaboration and varied skills, the desire for open source, shared code and evaluation, and a requirement for replicability through rigorous and open evaluation.
Automation has a great potential to improve the speed of systematic reviews. Considerable work is already being done on many of the steps involved in a review. The {\textquoteright}Vienna Principles{\textquoteright} set out in this paper aim to guide a more coordinated effort which will allow the integration of work by separate teams and build on the experience, code and evaluations done by the many teams working across the globe.},
	keywords = {automation, Collaboration, Systematic review},
	doi = {10.1186/s13643-018-0740-7},
	author = {Elaine Beller and Justin Clark and Guy Tsafnat and Clive Adams and Heinz Diehl and Hans Lund and Mourad Ouzzani and Kristina Thayer and James Thomas and Tari Turner and Jun Xia and Karen Robinson and Paul Glasziou}
}
@article {2018,
	title = {Robot vs. tax inspector or how the fourth industrial revolution will change the tax system: a review of problems and solutions},
	journal = {Journal of Tax Reform},
	volume = {4},
	year = {2018},
	month = {Jan-01-2018},
	pages = {6 - 26},
	abstract = {The fourth Industiral Revolution and the accelerated development of cyber-physical technologies lead to essential changes in national tax systems and international taxation. The main areas in which taxation meets cyber-physical technologies are digitization, robotization, M2M and blockchain technologies. Each of these areas has its own opportunities and problems. Three main approaches towards possible solutions for these new problems are identified. The first is to try to apply taxation to new cyber-physical technologies and products of their application. This approach includes the OECD{\textquoteright}s Action 1 plan on Base Erosion and Profit Shifting. It also includes the spread of traditional taxes on new objects - personal data, cryptocurrencies, imputed income of robots. The second is to replace digital transactions and shortfalls in revenues by traditional objects of taxation in the form of tangible assets and people and / or increase tax pressure (including by improving tax administration with use of Big Data) and the degree of progressiveness of taxes already levied on such objects. The third approach is to set a course on building a new tax space with smart taxes based on real-time principles, smart contracts and Big Data. This implies a transition to automatic taxation using blockchain technologies, which focus on the functions of applying distributed ledgers of business transactions in real-time. At present, the general trends are such that the first and second are prevalent, which is manifested in an increase in the relative importance of property, sales and employment taxes. Concerning the third approach, any movement in this direction is still facing a number oftechnical and other problems and is thus being discussed mainly at the conceptual level},
	keywords = {blockchain, cyber-physical technologies, digitization, taxes in Big Data, taxes on cryptocurrencies, taxes on digital goods, taxes on robots},
	issn = {24128872},
	doi = {10.15826/jtr.2018.4.1.042},
	url = {https://jtr.urfu.ru/en/archive/journal/95/article/1113/},
	author = {Vishnevsky, Valentine P. and Chekina, Viktoriia D.}
}
@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}
}
@conference {2018,
	title = {Towards a Sociological Conception of Artificial Intelligence},
	booktitle = {Artificial General Intelligence (AGI)},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Social sciences have been always formed and influenced by the development of society, adjusting the conceptual, methodological, and theoretical frameworks to emerging social phenomena. In recent years, with the leap in the advancement of Artificial Intelligence (AI) and the proliferation of its everyday applications, "non-human intelligent actors" are increasingly becoming part of the society. This is manifested in the evolving realms of smart home systems, autonomous vehicles, chatbots, intelligent public displays, etc. In this paper, we present a prospective research project that takes one of the pioneering steps towards establishing a "distinctively sociological" conception of AI. Its first objective is to extract the existing conceptions of AI as perceived by its technological developers and (possibly differently) by its users. In the second part, capitalizing on a set of interviews with experts from social science domains, we will explore the new imaginable conceptions of AI that do not originate from its technological possibilities but rather from societal necessities. The current formal ways of defining AI are grounded in the technological possibilities, namely, machine learning methods and neural network models. Buy what exactly is AI as a social phenomenon, which may act on its own, can be blamed responsible for ethically problematic behavior, or even endanger people{\textquoteright}s employment? We argue that such conceptual investigation is a crucial step for further empirical studies of phenomena related to AI{\textquoteright}s position in current societies, but also will open up ways for critiques of new technological advancements with social consequences in mind from the outset.},
	keywords = {artificial intelligence, Social sciences, Sociology},
	doi = {10.1007/978-3-319-97676-1_13},
	author = {Jakub Mlynar and Hamed S. Alavi and Himanshu Verma and Lorenzo Cantoni}
}
@conference {2018,
	title = {Work that Enables Care: Understanding Tasks, Automation, and the National Health Service},
	booktitle = {iConference},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Automation of jobs is discussed as a threat to many job occupations, but in the UK healthcare sector many view technology and automation as a way to save a threatened system. However, existing quantitative models that rely on occupation-level measures of the likelihood of automation suggest that few healthcare occupations are susceptible to automation. In order to improve these quantitative models, we focus on the potential impacts of task-level automation on health work, using qualitative ethnographic research to understand the mundane information work in general practices. By understanding the detailed tasks and variations of information work, we are building a more complete and accurate understanding of how healthcare staff work and interact with technology and with each other, often mediated by technology.},
	keywords = {automation, Ethnography, Primary care, Sociotechnical},
	doi = {10.1007/978-3-319-78105-1_60},
	author = {Matt WIllis and Eric T. Meyer}
}
@article {2018,
	title = {Working and organizing in the age of the learning algorithm},
	journal = {Information and Organization},
	volume = {28},
	year = {2018},
	publisher = {Elsevier},
	chapter = {62-70},
	abstract = {Learning algorithms, technologies that generate responses, classifications, or dynamic predictions that resemble those of a knowledge worker, raise important research questions for organizational scholars related to work and organizing. We suggest that such algorithms are distinguished by four consequential aspects: black-boxed performance, comprehensive digitization, anticipatory quantification, and hidden politics. These aspects are likely to alter work and organizing in qualitatively different ways beyond simply signaling an acceleration of long-term technology trends. Our analysis indicates that learning algorithms will transform expertise in organizations, reshape work and occupational boundaries, and offer novel forms of coordination and control. Thus, learning algorithms can be considered performative due to the extent to which their use can shape and alter work and organizational realities. Their rapid deployment requires scholarly attention to societal issues such as extent to which the algorithm is authorized to make decisions, the need to incorporate morality in the technology, and their digital iron-cage potential.},
	doi = {10.1016/j.inforandorg.2018.02.005},
	author = {Samer Faraj and Stella Pachidi and Karla Sayegh}
}
@conference {2017,
	title = {Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety},
	booktitle = {Scientific Methods in Academic Research and Teaching International Conference},
	year = {2017},
	pages = {184-197},
	abstract = {Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots - machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of {\textquoteright}Are we safe enough in the future of Artificial Intelligence?{\textquoteright}. In detail, the discussion includes a comprehensive focus on {\textquoteright}dystopic{\textquoteright} scenarios, enables interested researchers to think about some {\textquoteright}moral dilemmas{\textquoteright} and family have some ethical outputs that are considerable for developing good intelligent systems. From a general perspective, the discussion taken here is a good opportunity to improve awareness on the mentioned, remarkable research topics associated with not only Artificial Intelligence but also many other natural and social sciences taking role in the humankind},
	keywords = {artificial intelligence, artificial intelligence safety, future of artificial intelligence, machine ethics, machine learning},
	author = {Utku  K{\"o}se}
}
@article {2017,
	title = {Will Life Be Worth Living in a World Without Work? Technological Unemployment and the Meaning of Life},
	journal = {Science and Engineering Ethics},
	volume = {23},
	year = {2017},
	publisher = {Springer},
	chapter = {41-64},
	abstract = {Suppose we are about to enter an era of increasing technological unemployment. What implications does this have for society? Two distinct ethical/social issues would seem to arise. The first is one of distributive justice: how will the (presumed) efficiency gains from automated labour be distributed through society? The second is one of personal fulfillment and meaning: if people no longer have to work, what will they do with their lives? In this article, I set aside the first issue and focus on the second. In doing so, I make three arguments. First I argue that there are good reasons to embrace non-work and that these reasons become more compelling in an era of technological unemployment. Second, I argue that the technological advances that make widespread technological unemployment possible could still threaten or undermine human flourishing and meaning, especially if (as is to be expected) they do not remain confined to the economic sphere. And third, I argue that this threat could be contained if we adopt an integrative approach to our relationship with technology. In advancing these arguments, I draw on three distinct literatures: (1) the literature on technological unemployment and workplace automation; (2) the antiwork critique - which I argue gives reasons to embrace technological unemployment; and (3) the philosophical debate about the conditions for meaning in life - which I argue gives reasons for concern.},
	keywords = {Antiwork, automation, Egalitarianism, Freedom, Meaning of life, Technological unemployment, Transhumanism},
	doi = {10.1007s/11948-016-9770-5},
	author = {John Danaher}
}
@article {2016,
	title = {Human Interaction With Robot Swarms: A Survey},
	journal = {Transaction on human-machine systems},
	volume = {46},
	year = {2016},
	month = {02/2016},
	pages = {9-26},
	publisher = {IEEE},
	abstract = {Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarm. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.},
	keywords = {Human-robot interaction (HRI), human-swarm interaction (HSI), multi-robot systems, swarm robotics}
}
@article {2015,
	title = {Research Priorities for Robust and Beneficial Artificial Intelligence},
	journal = {Association for the Advancement of Artificial Intelligence},
	volume = {4},
	year = {2015},
	pages = {105-114},
	abstract = {<p>Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents - systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality - colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods can led to a large degree of integration and crossfertilization between AI, machine learning, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.</p>},
	url = {https://futureoflife.org/data/documents/research_priorities.pdf},
	author = {Stuart Russell and Daniel Dewey and Max Tegmark}
}
@proceedings {1995,
	title = {Complementary allocation of functions in automated work systems},
	volume = {20},
	year = {1995},
	pages = {989-994},
	publisher = {Elsevier},
	author = {G. Grote and S. Weik and T. Wafler and M. Zolch}
}
@proceedings {1995,
	title = {Integration of people, technology and organization: the european approach},
	volume = {20},
	year = {1995},
	pages = {957-961},
	publisher = {Elsevier},
	address = {Tokyo, Japan},
	abstract = {This paper presents the general outline of new method, HITOP-D, considering the integration and joint optimization of people, technology, and organization. This method is based on the existing american methods HITOP and ACTION. It takes in account the specific european industrial context. In an iterating process the preliminary design of a project is assessed according a list of criteria of the four aspects people, technology, organization, and task design. Incongruencies are solved through a fit analysis and redesigning the original project. The performance of HITOP-D will be empirically evaluated.},
	author = {Christina Kirsch and Peter Troxier and Eberhard Ulich}
}
@article {1994,
	title = {Design concepts of computer-aided integrated manufacturing systems: Work-psychological concepts and empirical findings},
	journal = {International Journal of Industrial Ergonomics},
	volume = {17},
	year = {1994},
	publisher = {Elsevier},
	chapter = {11-19},
	abstract = {The research project "GRIPS" is investigating the design of computer-aided integrated manufacturing systems from a work psychological perspective. The goal is to develop and empirically support adequate design concepts. The project consists of three phases. Evidence from a broad questionnaire survey indicates that most CIM implementations fail to meet expectations associated therewith. Based on the assumption that only the joint optimization of social and technical system results in humane working conditions and economic efficiency, implementations and use of CIM systems has been investigated in 60 companies in Switzerland. THe conceptual framework distinguishes technically-oriented and work-oriented design concepts on four levels; the enterprise, the organizational unit, the group and the individual. Work-oriented manufacturing systems - as opposed to technically-oriented ones - are characterized by decentralization, functional integration, work in self-regulated groups and complete and challenging tasks. The findings support the hypothesis that work-oriented design concepts are related to higher efficiency and better achievement of goals and pursued with the use of new technologies. In the third phase 12 companies have been selected for detailed case studies: The companies are comparable concerning product range and manufacturing conditions but different on the level of work-orientation.},
	keywords = {Computer-Integrated-Manufacturing CIM, Organizational design, Production design concepts, Socio-technical system approach, Work psychology, Work-orientation},
	author = {C. Kirsh and O. Strohm and E. Ulich}
}
