01178nas a2200445   4500008004100000022001400041245003900055210003600094260004200130300000800172490000700180653000700187653002800194653001400222653001300236653003100249653002100280653002200301653001400323653001500337653001100352653001800363653002300381653001500404653001600419653003400435653002200469653002500491653001500516653001600531653001300547653003300560653001500593653001800608653001900626653001600645100001800661700002300679856003000702       2020                        eng d  a1927-032100aEditorial: Insights (January 2020)0 aEditorial Insights January 2020  aOttawabTalent First Networkc01/2020  a3-40 v1010aAI10aartificial intelligence10aB2B sales10abig data10abusiness-to-business sales10adata-based value10adigital solutions10aecosystem10aecosystems10aEthics10aGujarat State10aIndian IT industry10ainnovation10aIT clusters10aKnowledge Innovation clusters10aNetworks Analysis10aregional development10aRoboethics10aSmart robot10astrategy10aSystematic literature review10atechnology10avalue capture10avalue creation10avalue sales1 aTanev, Stoyan1 aSandstrom, Gregory  utimreview.ca/article/129801906nas a2200205   4500008004100000022001400041245004200055210003900097260004200136300001000178490000700188520137700195653000701572653002801579653001101607653001501618653001601633100002101649856003001670       2020                        eng d  a1927-032100aAn Ethical Framework for Smart Robots0 aEthical Framework for Smart Robots  aOttawabTalent First Networkc01/2020  a35-440 v103 aThis article focuses on “roboethics” in the age of growing adoption of smart robots, which can now be seen as a new robotic “species”. As autonomous AI systems, they can collaborate with humans and are capable of learning from their operating environment, experiences, and human behaviour feedback in human-machine interaction. This enables smart robots to improve their performance and capabilities. This conceptual article reviews key perspectives to roboethics, as well as establishes a framework to illustrate its main ideas and features. Building on previous literature, roboethics has four major types of implications for smart robots: 1) smart robots as amoral and passive tools, 2) smart robots as recipients of ethical behavior in society, 3) smart robots as moral and active agents, and 4) smart robots as ethical impact-makers in society. The study contributes to current literature by suggesting that there are two underlying ethical and moral dimensions behind these perspectives, namely the “ethical agency of smart robots” and “object of moral judgment”, as well as what this could look like as smart robots become more widespread in society. The article concludes by suggesting how scientists and smart robot designers can benefit from a framework, discussing the limitations of the present study, and proposing avenues for future research.10aAI10aartificial intelligence10aEthics10aRoboethics10aSmart robot1 aWesterlund, Mika  utimreview.ca/article/131202715nas a2200217   4500008004100000022001400041245005400055210005400109260004200163300000900205490000600214520213200220653000702352653002902359653002802388653001202416653001502428653000802443100001602451856003002467       2019                        eng d  a1927-032100aArtificial Intelligence for Innovation in Austria0 aArtificial Intelligence for Innovation in Austria  aOttawabTalent First Networkc12/2019  a5-150 v93 aIt has been claimed that Artificial Intelligence (AI) carries enormous potential for service and product innovation. Policy makers world-wide nowadays aim to foster environments conducive for AI-based innovation. This paper addresses the current lack of empirical data for evidence-based innovation policies and the management of AI-based innovation. It focuses on &ldquo;AI and innovation management&rdquo; in addressing the question whether innovation that is based on new AI technology requires a management approach different from other forms of IT innovation. We present results from a study of Austrian companies on the degree of use and implementation of AI, and on challenges related to AI-based innovation management. This study used a keyword-list approach to define &ldquo;Artificial Intelligence&rdquo; and to find AI-based innovation projects in research databases. These projects facilitated the identification of experts from organisations developing AI-based innovation. In total, eleven experts were interviewed about their AI-based innovation activities. The results show that AI is a very fast emerging technology that is being applied in many sectors. A broad range of innovative solutions are being developed and some have already reached the market. Specific AI business models are, however, less clear and still developing. Companies are facing multiple challenges from regulation to human resources and data collection. Managing AI-based innovation will be particularly difficult for smaller enterprises, where problems are often more pronounced than in larger industries. Explicit challenges for managing AI-based innovations include the necessary attention to managing expectations and ensuring historic metadata expertise essential for many AI-based solutions. Policies to support AI-based innovation therefore should focus on human aspects. This includes increasing the availability of AI experts, but also concerns the development of new job profiles, such as experts in AI training. AI innovators also require clear AI regulation and research investments in key challenges, such as explainable AI.10aAI10aAI innovation management10aartificial intelligence10aAustria10ainnovation10aSME1 aPrem, Erich  utimreview.ca/article/1287